{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "400bb857-4dbd-4b1f-92d6-49ed08958536",
   "metadata": {},
   "source": [
    "## Code for Analyazing Emotion/Sentiment in Reddit Data\n",
    "\n",
    "*The following notebook contains code that was used in the analysis of emotional categories in Reddit Data.* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77ed136-0261-41db-87ce-09d5f38bd0f0",
   "metadata": {},
   "source": [
    "## Analyze Data Structure to Identify Sample Size Issues\n",
    "\n",
    "*As a result of this analysis, shows with less than 1k comments were removed*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a4beecb-113d-4fbb-9ad6-578cfecb49b8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load your data\n",
    "# Adjust the file path to match your file location and type\n",
    "df = pd.read_csv('Data/In_Sentiment_Percentages_By_Show.csv')  \n",
    "#df = pd.read_csv('Data/KDrama_In_Sentiment_Percentages_By_Show.csv')\n",
    "\n",
    "# ============================================================================\n",
    "# 1. BASIC DATA EXPLORATION\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"BASIC DATA SUMMARY\")\n",
    "print(\"=\"*80)\n",
    "print(f\"\\nTotal number of shows: {df['SHOW'].nunique()}\")\n",
    "print(f\"Total number of sentiment categories: {df['PREDICTION'].nunique()}\")\n",
    "print(f\"\\nFirst few rows:\")\n",
    "print(df.head(10))\n",
    "\n",
    "# ============================================================================\n",
    "# 2. SAMPLE SIZE DISTRIBUTION ACROSS SHOWS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SAMPLE SIZE DISTRIBUTION\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Calculate total comments per show\n",
    "show_totals = df.groupby('SHOW')['count'].sum().sort_values(ascending=False)\n",
    "\n",
    "print(f\"\\nSummary statistics for comments per show:\")\n",
    "print(show_totals.describe())\n",
    "\n",
    "print(f\"\\nShows with smallest samples:\")\n",
    "print(show_totals.tail(10))\n",
    "\n",
    "print(f\"\\nShows with largest samples:\")\n",
    "print(show_totals.head(10))\n",
    "\n",
    "# Create bins for sample sizes\n",
    "bins = [0, 100, 500, 1000, 5000, 10000, 50000]\n",
    "labels = ['<100', '100-500', '500-1K', '1K-5K', '5K-10K', '10K+']\n",
    "show_totals_df = pd.DataFrame({'total_comments': show_totals})\n",
    "show_totals_df['size_category'] = pd.cut(show_totals_df['total_comments'], \n",
    "                                           bins=bins, \n",
    "                                           labels=labels,\n",
    "                                           include_lowest=True)\n",
    "\n",
    "print(f\"\\nNumber of shows in each size category:\")\n",
    "print(show_totals_df['size_category'].value_counts().sort_index())\n",
    "\n",
    "# ============================================================================\n",
    "# 3. NEUTRAL VS NON-NEUTRAL BREAKDOWN\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"NEUTRAL VS NON-NEUTRAL SENTIMENT\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Identify neutral sentiment (adjust the exact string if needed)\n",
    "df['is_neutral'] = df['PREDICTION'].str.contains('Neutral', case=False, na=False)\n",
    "\n",
    "neutral_stats = df.groupby('SHOW').apply(\n",
    "    lambda x: pd.Series({\n",
    "        'total_comments': x['count'].sum(),\n",
    "        'neutral_comments': x[x['is_neutral']]['count'].sum(),\n",
    "        'non_neutral_comments': x[~x['is_neutral']]['count'].sum(),\n",
    "        'neutral_pct': (x[x['is_neutral']]['count'].sum() / x['count'].sum()) * 100\n",
    "    })\n",
    ").sort_values('total_comments', ascending=False)\n",
    "\n",
    "print(f\"\\nNeutral sentiment statistics across all shows:\")\n",
    "print(f\"Mean % neutral: {neutral_stats['neutral_pct'].mean():.2f}%\")\n",
    "print(f\"Median % neutral: {neutral_stats['neutral_pct'].median():.2f}%\")\n",
    "print(f\"Min % neutral: {neutral_stats['neutral_pct'].min():.2f}%\")\n",
    "print(f\"Max % neutral: {neutral_stats['neutral_pct'].max():.2f}%\")\n",
    "\n",
    "print(f\"\\nShows with smallest non-neutral comment counts:\")\n",
    "print(neutral_stats.nsmallest(10, 'non_neutral_comments')[['total_comments', 'non_neutral_comments', 'neutral_pct']])\n",
    "\n",
    "# ============================================================================\n",
    "# 4. EMOTION DISTRIBUTION VARIABILITY BY SAMPLE SIZE\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"EMOTION DISTRIBUTION VARIABILITY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Calculate coefficient of variation for each emotion across shows\n",
    "emotion_variability = []\n",
    "\n",
    "for emotion in df['PREDICTION'].unique():\n",
    "    emotion_data = df[df['PREDICTION'] == emotion].copy()\n",
    "    emotion_data = emotion_data.merge(show_totals_df, left_on='SHOW', right_index=True)\n",
    "    \n",
    "    if len(emotion_data) > 0:\n",
    "        cv = emotion_data['PERCENTAGES'].std() / emotion_data['PERCENTAGES'].mean() if emotion_data['PERCENTAGES'].mean() > 0 else np.nan\n",
    "        emotion_variability.append({\n",
    "            'emotion': emotion,\n",
    "            'mean_pct': emotion_data['PERCENTAGES'].mean(),\n",
    "            'std_pct': emotion_data['PERCENTAGES'].std(),\n",
    "            'cv': cv,\n",
    "            'n_shows': len(emotion_data)\n",
    "        })\n",
    "\n",
    "emotion_var_df = pd.DataFrame(emotion_variability).sort_values('mean_pct', ascending=False)\n",
    "print(\"\\nEmotion variability (sorted by mean percentage):\")\n",
    "print(emotion_var_df.to_string(index=False))\n",
    "\n",
    "# ============================================================================\n",
    "# 5. CONFIDENCE INTERVAL ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"CONFIDENCE INTERVAL WIDTHS FOR DIFFERENT SAMPLE SIZES\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# For a few example emotions, calculate confidence interval widths\n",
    "# Using Wilson score interval for proportions\n",
    "\n",
    "def wilson_ci_width(p, n, confidence=0.95):\n",
    "    \"\"\"Calculate width of Wilson score confidence interval\"\"\"\n",
    "    from scipy import stats\n",
    "    z = stats.norm.ppf((1 + confidence) / 2)\n",
    "    denominator = 1 + z**2 / n\n",
    "    center = (p + z**2 / (2*n)) / denominator\n",
    "    margin = z * np.sqrt(p * (1-p) / n + z**2 / (4*n**2)) / denominator\n",
    "    return 2 * margin  # Total width (upper - lower)\n",
    "\n",
    "# Select a few representative emotions (adjust as needed)\n",
    "example_emotions = df.groupby('PREDICTION')['PERCENTAGES'].mean().sort_values(ascending=False).head(5).index\n",
    "\n",
    "print(\"\\nConfidence interval widths (95%) for top 5 emotions at different sample sizes:\")\n",
    "print(\"(Smaller CI width = more reliable estimate)\\n\")\n",
    "\n",
    "sample_sizes = [50, 100, 500, 1000, 5000, 10000]\n",
    "for emotion in example_emotions:\n",
    "    emotion_mean_pct = df[df['PREDICTION'] == emotion]['PERCENTAGES'].mean()\n",
    "    p = emotion_mean_pct / 100  # Convert to proportion\n",
    "    print(f\"\\n{emotion} (avg ~{emotion_mean_pct:.2f}%):\")\n",
    "    for n in sample_sizes:\n",
    "        ci_width = wilson_ci_width(p, n) * 100  # Convert back to percentage points\n",
    "        print(f\"  n={n:5d}: CI width = ±{ci_width:.2f} percentage points\")\n",
    "\n",
    "# ============================================================================\n",
    "# 6. SPECIFIC EXAMPLES: SMALL VS LARGE SAMPLE SHOWS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"EXAMPLE COMPARISONS: SMALL VS LARGE SAMPLE SHOWS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Get one small and one large show\n",
    "small_show = show_totals.tail(1).index[0]\n",
    "large_show = show_totals.head(1).index[0]\n",
    "\n",
    "print(f\"\\nSmall sample show: {small_show} ({show_totals[small_show]} comments)\")\n",
    "print(df[df['SHOW'] == small_show][['PREDICTION', 'count', 'PERCENTAGES']].sort_values('count', ascending=False))\n",
    "\n",
    "print(f\"\\nLarge sample show: {large_show} ({show_totals[large_show]} comments)\")\n",
    "print(df[df['SHOW'] == large_show][['PREDICTION', 'count', 'PERCENTAGES']].sort_values('count', ascending=False))\n",
    "\n",
    "# ============================================================================\n",
    "# 7. VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "\n",
    "# Plot 1: Distribution of sample sizes\n",
    "ax1 = axes[0, 0]\n",
    "show_totals.plot(kind='hist', bins=30, ax=ax1, edgecolor='black')\n",
    "ax1.set_xlabel('Total Comments per Show')\n",
    "ax1.set_ylabel('Number of Shows')\n",
    "ax1.set_title('Distribution of Sample Sizes Across Shows')\n",
    "ax1.axvline(show_totals.median(), color='red', linestyle='--', label=f'Median: {show_totals.median():.0f}')\n",
    "ax1.legend()\n",
    "\n",
    "# Plot 2: Sample size categories\n",
    "ax2 = axes[0, 1]\n",
    "size_counts = show_totals_df['size_category'].value_counts().sort_index()\n",
    "size_counts.plot(kind='bar', ax=ax2, color='steelblue', edgecolor='black')\n",
    "ax2.set_xlabel('Sample Size Category')\n",
    "ax2.set_ylabel('Number of Shows')\n",
    "ax2.set_title('Shows by Sample Size Category')\n",
    "ax2.tick_params(axis='x', rotation=45)\n",
    "\n",
    "# Plot 3: Neutral percentage vs sample size\n",
    "ax3 = axes[1, 0]\n",
    "ax3.scatter(neutral_stats['total_comments'], neutral_stats['neutral_pct'], alpha=0.6)\n",
    "ax3.set_xlabel('Total Comments (log scale)')\n",
    "ax3.set_ylabel('% Neutral Sentiment')\n",
    "ax3.set_title('Neutral Sentiment % vs Sample Size')\n",
    "ax3.set_xscale('log')\n",
    "ax3.axhline(neutral_stats['neutral_pct'].mean(), color='red', linestyle='--', \n",
    "            label=f'Mean: {neutral_stats[\"neutral_pct\"].mean():.1f}%')\n",
    "ax3.legend()\n",
    "\n",
    "# Plot 4: Non-neutral comments available\n",
    "ax4 = axes[1, 1]\n",
    "neutral_stats['non_neutral_comments'].plot(kind='hist', bins=30, ax=ax4, \n",
    "                                            color='coral', edgecolor='black')\n",
    "ax4.set_xlabel('Non-Neutral Comments per Show')\n",
    "ax4.set_ylabel('Number of Shows')\n",
    "ax4.set_title('Distribution of Non-Neutral Comments Across Shows')\n",
    "ax4.axvline(neutral_stats['non_neutral_comments'].median(), color='red', \n",
    "            linestyle='--', label=f'Median: {neutral_stats[\"non_neutral_comments\"].median():.0f}')\n",
    "ax4.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('sentiment_sample_size_analysis.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'sentiment_sample_size_analysis.png'\")\n",
    "\n",
    "# ============================================================================\n",
    "# 8. RECOMMENDATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"RECOMMENDATIONS FOR HANDLING SMALL SAMPLES\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "min_threshold = 1000  # You can adjust this\n",
    "n_below_threshold = (show_totals < min_threshold).sum()\n",
    "pct_below_threshold = (n_below_threshold / len(show_totals)) * 100\n",
    "\n",
    "print(f\"\\nIf we set a minimum threshold of {min_threshold} comments:\")\n",
    "print(f\"  - {n_below_threshold} shows ({pct_below_threshold:.1f}%) would be excluded\")\n",
    "print(f\"  - {len(show_totals) - n_below_threshold} shows would remain\")\n",
    "\n",
    "print(f\"\\nMedian non-neutral comments for shows below threshold: \"\n",
    "      f\"{neutral_stats[neutral_stats['total_comments'] < min_threshold]['non_neutral_comments'].median():.0f}\")\n",
    "print(f\"Median non-neutral comments for shows above threshold: \"\n",
    "      f\"{neutral_stats[neutral_stats['total_comments'] >= min_threshold]['non_neutral_comments'].median():.0f}\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9174101f-a806-482f-b3c0-499c98f2b871",
   "metadata": {},
   "source": [
    "### Analyze Original Sentiment Predictions\n",
    "\n",
    "*We do not provide source data for this step as it violates terms of agreement with Reddit Research API*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b8d3874-5b29-4639-b738-963b5347f6d0",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import ast\n",
    "from scipy.special import softmax\n",
    "\n",
    "# Load your data\n",
    "# Adjust the file path to match your file location and type\n",
    "df = pd.read_csv('Data/InShow_Sentiment_GlobalHits.csv')  \n",
    "\n",
    "# ============================================================================\n",
    "# PARSE PREDICTION DICTIONARIES\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"SENTIMENT PREDICTION MARGIN ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nParsing sentiment predictions...\")\n",
    "\n",
    "# Convert string representation of dict to actual dict if needed\n",
    "if isinstance(df['SENTIMENT'].iloc[0], str):\n",
    "    df['SENTIMENT'] = df['SENTIMENT'].apply(ast.literal_eval)\n",
    "\n",
    "# ============================================================================\n",
    "# EXTRACT MARGIN INFORMATION\n",
    "# ============================================================================\n",
    "\n",
    "def extract_prediction_info(pred_dict):\n",
    "    \"\"\"\n",
    "    Extract useful information from prediction dictionary.\n",
    "    Returns: top emotion, top score, second emotion, second score, margin\n",
    "    \"\"\"\n",
    "    sorted_preds = sorted(pred_dict.items(), key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    top_emotion = sorted_preds[0][0]\n",
    "    top_score = sorted_preds[0][1]\n",
    "    \n",
    "    second_emotion = sorted_preds[1][0]\n",
    "    second_score = sorted_preds[1][1]\n",
    "    \n",
    "    margin = top_score - second_score\n",
    "    \n",
    "    # Also get probabilities using softmax\n",
    "    logits = np.array(list(pred_dict.values()))\n",
    "    probs = softmax(logits)\n",
    "    \n",
    "    # Get probability of top prediction\n",
    "    emotion_list = list(pred_dict.keys())\n",
    "    top_idx = emotion_list.index(top_emotion)\n",
    "    top_prob = probs[top_idx]\n",
    "    \n",
    "    return {\n",
    "        'top_emotion': top_emotion,\n",
    "        'top_score': top_score,\n",
    "        'top_prob': top_prob,\n",
    "        'second_emotion': second_emotion,\n",
    "        'second_score': second_score,\n",
    "        'margin': margin\n",
    "    }\n",
    "\n",
    "print(\"Extracting prediction margins for all comments...\")\n",
    "pred_info = df['SENTIMENT'].apply(extract_prediction_info)\n",
    "pred_info_df = pd.DataFrame(pred_info.tolist())\n",
    "\n",
    "# Combine with original data\n",
    "df_analysis = pd.concat([df[['SHOW']], pred_info_df], axis=1)\n",
    "\n",
    "# ============================================================================\n",
    "# OVERALL MARGIN STATISTICS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"MARGIN STATISTICS (Top Score - Second Score)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nOverall margin statistics:\")\n",
    "print(df_analysis['margin'].describe())\n",
    "\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "print(\"Margin statistics by top emotion:\")\n",
    "print(\"-\"*80)\n",
    "margin_by_emotion = df_analysis.groupby('top_emotion')['margin'].agg([\n",
    "    'count', 'mean', 'median', 'std', 'min', 'max'\n",
    "]).sort_values('mean', ascending=False)\n",
    "print(margin_by_emotion)\n",
    "\n",
    "# ============================================================================\n",
    "# NEUTRAL PREDICTIONS ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"NEUTRAL PREDICTION ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "neutral_preds = df_analysis[df_analysis['top_emotion'] == 'Neutral'].copy()\n",
    "non_neutral_preds = df_analysis[df_analysis['top_emotion'] != 'Neutral'].copy()\n",
    "\n",
    "print(f\"\\nTotal predictions: {len(df_analysis):,}\")\n",
    "print(f\"Neutral predictions: {len(neutral_preds):,} ({len(neutral_preds)/len(df_analysis)*100:.1f}%)\")\n",
    "print(f\"Non-neutral predictions: {len(non_neutral_preds):,} ({len(non_neutral_preds)/len(df_analysis)*100:.1f}%)\")\n",
    "\n",
    "print(\"\\nMargin comparison:\")\n",
    "print(f\"Neutral predictions - mean margin: {neutral_preds['margin'].mean():.3f}\")\n",
    "print(f\"Neutral predictions - median margin: {neutral_preds['margin'].median():.3f}\")\n",
    "print(f\"Non-neutral predictions - mean margin: {non_neutral_preds['margin'].mean():.3f}\")\n",
    "print(f\"Non-neutral predictions - median margin: {non_neutral_preds['margin'].median():.3f}\")\n",
    "\n",
    "print(\"\\nNeutral margin distribution:\")\n",
    "print(neutral_preds['margin'].describe())\n",
    "\n",
    "print(\"\\nWhen Neutral wins, what emotion comes in second?\")\n",
    "second_emotion_counts = neutral_preds['second_emotion'].value_counts()\n",
    "print(second_emotion_counts.head(15))\n",
    "\n",
    "# ============================================================================\n",
    "# PROBABILITY ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"PROBABILITY ANALYSIS (using softmax)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nTop prediction probability statistics:\")\n",
    "print(f\"Overall mean: {df_analysis['top_prob'].mean():.3f}\")\n",
    "print(f\"Neutral mean: {neutral_preds['top_prob'].mean():.3f}\")\n",
    "print(f\"Non-neutral mean: {non_neutral_preds['top_prob'].mean():.3f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# CLOSE CALL ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"CLOSE CALL ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Define thresholds for \"close calls\"\n",
    "thresholds = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0]\n",
    "\n",
    "print(\"\\nNeutral predictions with small margins:\")\n",
    "print(\"(These are the 'barely neutral' cases where second emotion might be more meaningful)\\n\")\n",
    "\n",
    "for threshold in thresholds:\n",
    "    close_neutral = neutral_preds[neutral_preds['margin'] < threshold]\n",
    "    pct = len(close_neutral) / len(neutral_preds) * 100\n",
    "    pct_total = len(close_neutral) / len(df_analysis) * 100\n",
    "    print(f\"Margin < {threshold}: {len(close_neutral):,} cases \"\n",
    "          f\"({pct:.1f}% of Neutral, {pct_total:.1f}% of all predictions)\")\n",
    "\n",
    "# ============================================================================\n",
    "# TEST ALTERNATIVE STRATEGIES\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"TESTING ALTERNATIVE ASSIGNMENT STRATEGIES\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "def reassign_with_threshold(row, neutral_margin_threshold):\n",
    "    \"\"\"\n",
    "    If Neutral wins by less than threshold, assign second emotion instead.\n",
    "    \"\"\"\n",
    "    if row['top_emotion'] == 'Neutral' and row['margin'] < neutral_margin_threshold:\n",
    "        return row['second_emotion']\n",
    "    else:\n",
    "        return row['top_emotion']\n",
    "\n",
    "# Test different thresholds\n",
    "test_thresholds = [0.5, 1.0, 1.5, 2.0, 3.0]\n",
    "\n",
    "print(\"\\nComparing emotion distributions under different threshold strategies:\\n\")\n",
    "print(f\"{'Strategy':<25} {'Neutral %':<12} {'Top Non-Neutral Emotion':<30} {'%':<8}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Original (argmax) strategy\n",
    "original_dist = df_analysis['top_emotion'].value_counts(normalize=True) * 100\n",
    "print(f\"{'Original (argmax)':<25} {original_dist.get('Neutral', 0):>10.2f}%  \"\n",
    "      f\"{original_dist.drop('Neutral', errors='ignore').index[0]:<30} \"\n",
    "      f\"{original_dist.drop('Neutral', errors='ignore').iloc[0]:>6.2f}%\")\n",
    "\n",
    "# Test each threshold\n",
    "for threshold in test_thresholds:\n",
    "    df_analysis[f'reassigned_{threshold}'] = df_analysis.apply(\n",
    "        lambda row: reassign_with_threshold(row, threshold), axis=1\n",
    "    )\n",
    "    new_dist = df_analysis[f'reassigned_{threshold}'].value_counts(normalize=True) * 100\n",
    "    \n",
    "    print(f\"{'Threshold=' + str(threshold):<25} {new_dist.get('Neutral', 0):>10.2f}%  \"\n",
    "          f\"{new_dist.drop('Neutral', errors='ignore').index[0]:<30} \"\n",
    "          f\"{new_dist.drop('Neutral', errors='ignore').iloc[0]:>6.2f}%\")\n",
    "\n",
    "# ============================================================================\n",
    "# PER-SHOW ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"PER-SHOW MARGIN ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "show_stats = df_analysis.groupby('SHOW').agg({\n",
    "    'margin': ['mean', 'median', 'std'],\n",
    "    'top_emotion': 'count'\n",
    "}).round(3)\n",
    "show_stats.columns = ['mean_margin', 'median_margin', 'std_margin', 'n_comments']\n",
    "show_stats = show_stats.sort_values('mean_margin', ascending=False)\n",
    "\n",
    "print(\"\\nShows with highest average margins (most confident predictions):\")\n",
    "print(show_stats.head(10))\n",
    "\n",
    "print(\"\\nShows with lowest average margins (least confident predictions):\")\n",
    "print(show_stats.tail(10))\n",
    "\n",
    "# Neutral margin by show\n",
    "neutral_by_show = neutral_preds.groupby('SHOW').agg({\n",
    "    'margin': ['mean', 'median', 'count']\n",
    "}).round(3)\n",
    "neutral_by_show.columns = ['neutral_mean_margin', 'neutral_median_margin', 'n_neutral']\n",
    "neutral_by_show = neutral_by_show.sort_values('neutral_mean_margin')\n",
    "\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "print(\"\\nShows with smallest neutral margins (most 'barely neutral' predictions):\")\n",
    "print(neutral_by_show.head(10))\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(3, 2, figsize=(15, 16))\n",
    "\n",
    "# Plot 1: Distribution of margins overall\n",
    "ax1 = axes[0, 0]\n",
    "ax1.hist(df_analysis['margin'], bins=50, edgecolor='black', alpha=0.7)\n",
    "ax1.axvline(df_analysis['margin'].mean(), color='red', linestyle='--', \n",
    "           linewidth=2, label=f'Mean: {df_analysis[\"margin\"].mean():.2f}')\n",
    "ax1.axvline(df_analysis['margin'].median(), color='orange', linestyle='--', \n",
    "           linewidth=2, label=f'Median: {df_analysis[\"margin\"].median():.2f}')\n",
    "ax1.set_xlabel('Margin (Top Score - Second Score)')\n",
    "ax1.set_ylabel('Number of Predictions')\n",
    "ax1.set_title('Distribution of Prediction Margins (All Predictions)')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 2: Neutral vs Non-neutral margins\n",
    "ax2 = axes[0, 1]\n",
    "ax2.hist([neutral_preds['margin'], non_neutral_preds['margin']], \n",
    "         bins=50, label=['Neutral', 'Non-neutral'], alpha=0.7, edgecolor='black')\n",
    "ax2.set_xlabel('Margin (Top Score - Second Score)')\n",
    "ax2.set_ylabel('Number of Predictions')\n",
    "ax2.set_title('Margin Distribution: Neutral vs Non-Neutral')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 3: Cumulative distribution of neutral margins\n",
    "ax3 = axes[1, 0]\n",
    "sorted_margins = np.sort(neutral_preds['margin'])\n",
    "cumulative = np.arange(1, len(sorted_margins) + 1) / len(sorted_margins) * 100\n",
    "ax3.plot(sorted_margins, cumulative, linewidth=2)\n",
    "ax3.set_xlabel('Margin Threshold')\n",
    "ax3.set_ylabel('% of Neutral Predictions Below Threshold')\n",
    "ax3.set_title('Cumulative Distribution: Neutral Prediction Margins')\n",
    "ax3.grid(True, alpha=0.3)\n",
    "# Add vertical lines for potential thresholds\n",
    "for thresh in [0.5, 1.0, 1.5, 2.0]:\n",
    "    pct = (neutral_preds['margin'] < thresh).sum() / len(neutral_preds) * 100\n",
    "    ax3.axvline(thresh, color='red', linestyle=':', alpha=0.5)\n",
    "    ax3.text(thresh, pct + 2, f'{thresh}\\n({pct:.1f}%)', \n",
    "            ha='center', fontsize=8)\n",
    "\n",
    "# Plot 4: Top probability distribution\n",
    "ax4 = axes[1, 1]\n",
    "ax4.hist([neutral_preds['top_prob'], non_neutral_preds['top_prob']], \n",
    "         bins=50, label=['Neutral', 'Non-neutral'], alpha=0.7, edgecolor='black')\n",
    "ax4.set_xlabel('Probability of Top Prediction (softmax)')\n",
    "ax4.set_ylabel('Number of Predictions')\n",
    "ax4.set_title('Confidence Distribution: Neutral vs Non-Neutral')\n",
    "ax4.legend()\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 5: When Neutral wins, what's second?\n",
    "ax5 = axes[2, 0]\n",
    "top_seconds = second_emotion_counts.head(15)\n",
    "ax5.barh(range(len(top_seconds)), top_seconds.values, color='steelblue', edgecolor='black')\n",
    "ax5.set_yticks(range(len(top_seconds)))\n",
    "ax5.set_yticklabels(top_seconds.index)\n",
    "ax5.set_xlabel('Count')\n",
    "ax5.set_title('When Neutral Wins, What Emotion is Second?')\n",
    "ax5.grid(True, alpha=0.3, axis='x')\n",
    "\n",
    "# Plot 6: Effect of different thresholds on Neutral percentage\n",
    "ax6 = axes[2, 1]\n",
    "threshold_range = np.linspace(0, 5, 50)\n",
    "neutral_pcts = []\n",
    "for t in threshold_range:\n",
    "    reassigned = df_analysis.apply(lambda row: reassign_with_threshold(row, t), axis=1)\n",
    "    neutral_pct = (reassigned == 'Neutral').sum() / len(reassigned) * 100\n",
    "    neutral_pcts.append(neutral_pct)\n",
    "\n",
    "ax6.plot(threshold_range, neutral_pcts, linewidth=2, color='darkblue')\n",
    "ax6.axhline(original_dist.get('Neutral', 0), color='red', linestyle='--', \n",
    "           label=f'Original: {original_dist.get(\"Neutral\", 0):.1f}%')\n",
    "ax6.set_xlabel('Neutral Margin Threshold')\n",
    "ax6.set_ylabel('% Predictions Assigned as Neutral')\n",
    "ax6.set_title('Effect of Threshold on Neutral Assignment')\n",
    "ax6.legend()\n",
    "ax6.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('sentiment_margin_analysis.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'sentiment_margin_analysis.png'\")\n",
    "\n",
    "# ============================================================================\n",
    "# RECOMMENDATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"RECOMMENDATIONS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Find a reasonable threshold based on data\n",
    "# Look for natural break points or use percentiles\n",
    "percentiles = [10, 25, 50, 75, 90]\n",
    "print(\"\\nNeutral margin percentiles:\")\n",
    "for p in percentiles:\n",
    "    val = np.percentile(neutral_preds['margin'], p)\n",
    "    count = (neutral_preds['margin'] < val).sum()\n",
    "    print(f\"  {p}th percentile: {val:.3f} ({count:,} predictions)\")\n",
    "\n",
    "# Calculate what threshold would reassign ~10%, ~25% of neutral predictions\n",
    "target_reassign_pcts = [5, 10, 15, 25]\n",
    "print(\"\\nThresholds to reassign specific percentages of Neutral predictions:\")\n",
    "for pct in target_reassign_pcts:\n",
    "    threshold = np.percentile(neutral_preds['margin'], pct)\n",
    "    n_affected = (neutral_preds['margin'] < threshold).sum()\n",
    "    print(f\"  To reassign {pct}% of Neutral: threshold ≈ {threshold:.2f} \"\n",
    "          f\"(affects {n_affected:,} predictions)\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)\n",
    "print(\"\\nNext steps:\")\n",
    "print(\"1. Review the margin distributions to identify a reasonable threshold\")\n",
    "print(\"2. Consider the trade-off: lower threshold = fewer reassignments but higher confidence\")\n",
    "print(\"3. Test your chosen threshold's effect on diversity metrics\")\n",
    "print(\"4. Compare results between original and reassigned strategies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b5c63c-39ba-4674-a8f4-5a2317dfb913",
   "metadata": {},
   "source": [
    "### Validation Step (Create Samples for Annotation)\n",
    "\n",
    "*We do not provide source data for this step as it violates terms of agreement with Reddit Research API*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b03ec772-395e-4555-9409-5d8155d5e63b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ast\n",
    "\n",
    "# Load your data with predictions\n",
    "df = pd.read_csv('Data/InShow_Sentiment_GlobalHits.csv')\n",
    "\n",
    "# Parse sentiment dictionaries if needed\n",
    "if isinstance(df['SENTIMENT'].iloc[0], str):\n",
    "    df['SENTIMENT'] = df['SENTIMENT'].apply(ast.literal_eval)\n",
    "\n",
    "# ============================================================================\n",
    "# EXTRACT PREDICTION INFO\n",
    "# ============================================================================\n",
    "\n",
    "def extract_prediction_info(pred_dict):\n",
    "    \"\"\"Extract top and second predictions with scores.\"\"\"\n",
    "    sorted_preds = sorted(pred_dict.items(), key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    return {\n",
    "        'top_emotion': sorted_preds[0][0],\n",
    "        'top_score': sorted_preds[0][1],\n",
    "        'second_emotion': sorted_preds[1][0],\n",
    "        'second_score': sorted_preds[1][1],\n",
    "        'margin': sorted_preds[0][1] - sorted_preds[1][1]\n",
    "    }\n",
    "\n",
    "print(\"Extracting prediction information...\")\n",
    "pred_info = df['SENTIMENT'].apply(extract_prediction_info)\n",
    "pred_info_df = pd.DataFrame(pred_info.tolist())\n",
    "\n",
    "# Combine with original data\n",
    "df_full = pd.concat([df, pred_info_df], axis=1)\n",
    "\n",
    "# ============================================================================\n",
    "# FILTER FOR VALIDATION SAMPLE\n",
    "# ============================================================================\n",
    "\n",
    "# Set your threshold\n",
    "THRESHOLD = 1.7  # Adjust this to match your chosen threshold\n",
    "\n",
    "print(f\"\\nFiltering for Neutral predictions with margin < {THRESHOLD}...\")\n",
    "\n",
    "# Get neutral predictions below threshold\n",
    "validation_candidates = df_full[\n",
    "    (df_full['top_emotion'] == 'Neutral') & \n",
    "    (df_full['margin'] < THRESHOLD)\n",
    "].copy()\n",
    "\n",
    "print(f\"Found {len(validation_candidates):,} candidates for validation\")\n",
    "\n",
    "# ============================================================================\n",
    "# STRATIFIED SAMPLING\n",
    "# ============================================================================\n",
    "\n",
    "# Sample strategy 1: Pure random sample\n",
    "SAMPLE_SIZE = 100\n",
    "random_sample = validation_candidates.sample(n=min(SAMPLE_SIZE, len(validation_candidates)), \n",
    "                                             random_state=42)\n",
    "\n",
    "# Sample strategy 2: Stratified by second emotion\n",
    "# (ensures you see a variety of what emotions would be reassigned to)\n",
    "print(\"\\nGenerating stratified sample by second emotion...\")\n",
    "\n",
    "# Get top second emotions\n",
    "second_emotion_counts = validation_candidates['second_emotion'].value_counts()\n",
    "print(f\"\\nTop 10 second emotions in this margin range:\")\n",
    "print(second_emotion_counts.head(10))\n",
    "\n",
    "# Stratified sampling: get proportional representation of each second emotion\n",
    "stratified_samples = []\n",
    "for emotion in second_emotion_counts.head(10).index:  # Top 10 emotions\n",
    "    emotion_subset = validation_candidates[validation_candidates['second_emotion'] == emotion]\n",
    "    # Sample proportionally, with minimum of 5 per emotion\n",
    "    n_samples = max(5, int(SAMPLE_SIZE * len(emotion_subset) / len(validation_candidates)))\n",
    "    if len(emotion_subset) >= n_samples:\n",
    "        sample = emotion_subset.sample(n=n_samples, random_state=42)\n",
    "        stratified_samples.append(sample)\n",
    "\n",
    "stratified_sample = pd.concat(stratified_samples).head(SAMPLE_SIZE)\n",
    "\n",
    "# Sample strategy 3: Edge cases - very small margins\n",
    "print(\"\\nGenerating edge case sample (smallest margins)...\")\n",
    "edge_cases = validation_candidates.nsmallest(50, 'margin')\n",
    "\n",
    "# ============================================================================\n",
    "# CREATE VALIDATION SPREADSHEET\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\nCreating validation samples...\")\n",
    "\n",
    "# Add the actual comment text column if it exists\n",
    "# Adjust column name to match your data\n",
    "possible_text_columns = ['IN_CHAR_CONTEXT']\n",
    "text_column = None\n",
    "for col in possible_text_columns:\n",
    "    if col in df.columns:\n",
    "        text_column = col\n",
    "        break\n",
    "\n",
    "if text_column is None:\n",
    "    print(\"\\nWARNING: Could not find text/comment column. Please add it manually.\")\n",
    "    print(f\"Available columns: {df.columns.tolist()}\")\n",
    "\n",
    "# Build validation columns - only the ones that exist in the dataframe\n",
    "validation_columns = [\n",
    "    'SHOW',\n",
    "    'top_emotion',\n",
    "    'top_score', \n",
    "    'second_emotion',\n",
    "    'second_score',\n",
    "    'margin'\n",
    "]\n",
    "\n",
    "# Add text column if found\n",
    "if text_column:\n",
    "    validation_columns.insert(1, text_column)  # Add after SHOW\n",
    "\n",
    "# Create three validation files\n",
    "samples = {\n",
    "    'random_sample': random_sample,\n",
    "    'stratified_sample': stratified_sample, \n",
    "    'edge_cases': edge_cases\n",
    "}\n",
    "\n",
    "for sample_name, sample_df in samples.items():\n",
    "    # Create validation dataframe - only select existing columns\n",
    "    validation_df = sample_df[validation_columns].copy()\n",
    "    \n",
    "    # NOW add empty columns for validation\n",
    "    validation_df['human_rating'] = ''\n",
    "    validation_df['notes'] = ''\n",
    "    \n",
    "    # Sort by margin (smallest first - most interesting cases)\n",
    "    validation_df = validation_df.sort_values('margin')\n",
    "    \n",
    "    # Save to CSV\n",
    "    filename = f'validation_sample_{sample_name}.csv'\n",
    "    validation_df.to_csv(filename, index=False)\n",
    "    print(f\"\\n✓ Saved {len(validation_df)} examples to '{filename}'\")\n",
    "\n",
    "# ============================================================================\n",
    "# CREATE A COMBINED MASTER SAMPLE\n",
    "# ============================================================================\n",
    "\n",
    "# Combine all three, remove duplicates, take top 100-150\n",
    "combined = pd.concat([random_sample, stratified_sample, edge_cases])\n",
    "# Drop duplicates by index\n",
    "combined = combined[~combined.index.duplicated(keep='first')]\n",
    "combined = combined.sort_values('margin')\n",
    "\n",
    "# Take up to 150 examples\n",
    "master_sample = combined.head(150)\n",
    "validation_master = master_sample[validation_columns].copy()\n",
    "validation_master['human_rating'] = ''\n",
    "validation_master['notes'] = ''\n",
    "validation_master['sample_source'] = ''  # Track which sample(s) it came from\n",
    "\n",
    "# Mark which sample each came from\n",
    "for idx in validation_master.index:\n",
    "    sources = []\n",
    "    if idx in random_sample.index:\n",
    "        sources.append('random')\n",
    "    if idx in stratified_sample.index:\n",
    "        sources.append('stratified')\n",
    "    if idx in edge_cases.index:\n",
    "        sources.append('edge')\n",
    "    validation_master.loc[idx, 'sample_source'] = '+'.join(sources)\n",
    "\n",
    "filename = 'validation_sample_MASTER.csv'\n",
    "validation_master.to_csv(filename, index=False)\n",
    "print(f\"\\n✓ Saved {len(validation_master)} examples to '{filename}' (MASTER FILE)\")\n",
    "\n",
    "# ============================================================================\n",
    "# VALIDATION INSTRUCTIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"VALIDATION INSTRUCTIONS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "instructions = \"\"\"\n",
    "Three validation files have been created:\n",
    "\n",
    "1. validation_sample_random_sample.csv - Random sample across all margin levels\n",
    "2. validation_sample_stratified_sample.csv - Balanced across different second emotions\n",
    "3. validation_sample_edge_cases.csv - Smallest margins (most ambiguous cases)\n",
    "4. validation_sample_MASTER.csv - Combined, deduplicated master file (RECOMMENDED)\n",
    "\n",
    "HOW TO VALIDATE:\n",
    "\n",
    "For each row, read the comment text and fill in the 'human_rating' column with one of:\n",
    "\n",
    "- 'neutral_correct' = Neutral is the right classification, don't reassign\n",
    "- 'second_better' = The second emotion is better, should be reassigned\n",
    "- 'other' = Neither is great, or some other emotion would be better\n",
    "- 'ambiguous' = Could go either way, no clear winner\n",
    "\n",
    "Optionally add notes in the 'notes' column about why you made that choice.\n",
    "\n",
    "WHAT TO LOOK FOR:\n",
    "\n",
    "- Is the comment truly neutral/factual, or does it have emotional content?\n",
    "- If emotional, does the second emotion capture it well?\n",
    "- Are there patterns (e.g., certain shows, certain second emotions more accurate)?\n",
    "\n",
    "ANALYSIS AFTER VALIDATION:\n",
    "\n",
    "After filling in ratings, calculate:\n",
    "- % where 'second_better' → reassignment is justified\n",
    "- % where 'neutral_correct' → threshold too aggressive\n",
    "- % 'other' or 'ambiguous' → might need different approach\n",
    "\n",
    "If 'second_better' > 60-70%, threshold seems reasonable.\n",
    "If 'neutral_correct' > 40%, threshold might be too high.\n",
    "\n",
    "TIPS:\n",
    "\n",
    "- Start with edge_cases (smallest margins) - these are the most interesting\n",
    "- If edge cases look good, the higher-margin cases will be even clearer\n",
    "- Look for patterns in which second emotions work well vs poorly\n",
    "\"\"\"\n",
    "\n",
    "print(instructions)\n",
    "\n",
    "# Save instructions to text file\n",
    "with open('VALIDATION_INSTRUCTIONS.txt', 'w') as f:\n",
    "    f.write(instructions)\n",
    "\n",
    "print(\"\\n✓ Instructions saved to 'VALIDATION_INSTRUCTIONS.txt'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"VALIDATION SAMPLE GENERATION COMPLETE\")\n",
    "print(\"=\"*80)\n",
    "print(f\"\\nRecommendation: Start with 'validation_sample_MASTER.csv'\")\n",
    "print(f\"This file contains {len(validation_master)} examples sorted by margin (smallest first)\")\n",
    "print(\"\\nYou can validate as many or as few as you have time for.\")\n",
    "print(\"Even 50 examples will give you useful signal!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9bade1e-8c07-486c-bb0c-bfaf336f3206",
   "metadata": {},
   "source": [
    "## Recompute Sentiment Prediction Score with Final Form of Dataset\n",
    "\n",
    "*Removed 'Black Mirror' and kept only 'Crash Landing on You' and 'Sweet Home' from Kdrama subset*\n",
    "\n",
    "*We do not provide source data for this step as it violates terms of agreement with Reddit Research API*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4a7ba863-dd93-4474-8a27-1162d12c991b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "APPLYING EMOTION REASSIGNMENT WITH THRESHOLD = 1.7\n",
      "================================================================================\n",
      "\n",
      "Applying reassignment threshold of 1.7...\n",
      "This will reassign Neutral predictions where margin < 1.7 to the second emotion\n",
      "\n",
      "Original Neutral predictions: 1,832,951\n",
      "New Neutral predictions: 1,559,068\n",
      "Reassigned: 273,883 (14.9% of Neutral)\n",
      "Total predictions: 2,488,053\n",
      "\n",
      "✓ Saved dataset with PREDICTION_2 column to 'Sentiment_Data/Final_InSentiment_Pred2.csv'\n",
      "\n",
      "================================================================================\n",
      "GENERATING EMOTION DISTRIBUTION BY SHOW\n",
      "================================================================================\n",
      "\n",
      "Generated emotion distribution for 50 shows\n",
      "Total rows: 1309\n",
      "\n",
      "Sample output for '13 Reasons Why':\n",
      "          SHOW            PREDICTION  count  PERCENTAGES\n",
      "13 Reasons Why       [27, 'Neutral']  17875    55.174862\n",
      "13 Reasons Why      [3, 'Annoyance']   1788     5.519030\n",
      "13 Reasons Why   [10, 'Disapproval']   1545     4.768960\n",
      "13 Reasons Why       [4, 'Approval']   1517     4.682532\n",
      "13 Reasons Why       [25, 'Sadness']   1288     3.975677\n",
      "13 Reasons Why     [0, 'Admiration']   1071     3.305862\n",
      "13 Reasons Why          [18, 'Love']    930     2.870636\n",
      "13 Reasons Why      [6, 'Confusion']    897     2.768775\n",
      "13 Reasons Why          [2, 'Anger']    763     2.355156\n",
      "13 Reasons Why [9, 'Disappointment']    587     1.811896\n",
      "13 Reasons Why      [1, 'Amusement']    566     1.747075\n",
      "13 Reasons Why       [11, 'Disgust']    489     1.509399\n",
      "13 Reasons Why   [22, 'Realization']    457     1.410624\n",
      "13 Reasons Why      [7, 'Curiosity']    432     1.333457\n",
      "13 Reasons Why          [14, 'Fear']    318     0.981572\n",
      "13 Reasons Why       [24, 'Remorse']    318     0.981572\n",
      "13 Reasons Why           [17, 'Joy']    310     0.956879\n",
      "13 Reasons Why         [8, 'Desire']    288     0.888971\n",
      "13 Reasons Why      [26, 'Surprise']    276     0.851931\n",
      "13 Reasons Why      [20, 'Optimism']    255     0.787110\n",
      "13 Reasons Why         [5, 'Caring']    175     0.540173\n",
      "13 Reasons Why    [13, 'Excitement']     80     0.246936\n",
      "13 Reasons Why [12, 'Embarrassment']     71     0.219156\n",
      "13 Reasons Why     [15, 'Gratitude']     70     0.216069\n",
      "13 Reasons Why   [19, 'Nervousness']     26     0.080254\n",
      "13 Reasons Why         [21, 'Pride']      4     0.012347\n",
      "13 Reasons Why        [23, 'Relief']      1     0.003087\n",
      "\n",
      "✓ Saved emotion distribution to 'Sentiment_Data/Final_InSentiment_ByShow.csv'\n",
      "\n",
      "================================================================================\n",
      "COMPARISON: ORIGINAL vs REASSIGNED\n",
      "================================================================================\n",
      "\n",
      "Average Neutral percentage across shows:\n",
      "  Original: 71.61%\n",
      "  Reassigned: 60.79%\n",
      "  Difference: 10.82 percentage points\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Emotions that gained the most from reassignment:\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Top 10 emotions that gained:\n",
      "                       original_count  new_count  difference  pct_change\n",
      "PREDICTION_NORMALIZED                                                   \n",
      "Approval                        52954     107490       54536  102.987499\n",
      "Disapproval                     49479      90423       40944   82.750258\n",
      "Annoyance                       51756      84293       32537   62.866141\n",
      "Confusion                       43572      68399       24827   56.979253\n",
      "Curiosity                       15938      33620       17682  110.942402\n",
      "Realization                     18850      34067       15217   80.726790\n",
      "Admiration                      82003      96380       14377   17.532285\n",
      "Disappointment                  22880      35453       12573   54.951923\n",
      "Sadness                         38462      50511       12049   31.327024\n",
      "Love                            67373      73586        6213    9.221795\n",
      "\n",
      "Neutral (should decrease):\n",
      "                       original_count  new_count  difference  pct_change\n",
      "PREDICTION_NORMALIZED                                                   \n",
      "Neutral                       1832951    1559068     -273883  -14.942189\n",
      "\n",
      "================================================================================\n",
      "REASSIGNMENT COMPLETE\n",
      "================================================================================\n",
      "\n",
      "Output files created:\n",
      "  1. Sentiment_Data/Final_InSentiment_Pred2.csv - Full dataset with PREDICTION_2 column\n",
      "  2. Sentiment_Data/Final_InSentiment_ByShow.csv - Emotion distribution by show (ready for analysis)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ast\n",
    "\n",
    "# Load your data with predictions\n",
    "df = pd.read_csv('Data/Final_InSentiment_Pred1.csv')\n",
    "\n",
    "# Parse sentiment dictionaries if needed\n",
    "if isinstance(df['SENTIMENT'].iloc[0], str):\n",
    "    df['SENTIMENT'] = df['SENTIMENT'].apply(ast.literal_eval)\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"APPLYING EMOTION REASSIGNMENT WITH THRESHOLD = 1.7\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# ============================================================================\n",
    "# STEP 1: APPLY REASSIGNMENT THRESHOLD\n",
    "# ============================================================================\n",
    "\n",
    "def reassign_emotion(row, threshold=1.7):\n",
    "    \"\"\"\n",
    "    Apply reassignment logic:\n",
    "    - If top emotion is Neutral and margin < threshold, return second emotion\n",
    "    - Otherwise, return top emotion\n",
    "    \n",
    "    Returns the emotion in the original format (e.g., \"[0, 'Admiration']\")\n",
    "    \"\"\"\n",
    "    pred_dict = row['SENTIMENT']\n",
    "    original_pred = row['PREDICTION']\n",
    "    \n",
    "    # Sort predictions by score\n",
    "    sorted_preds = sorted(pred_dict.items(), key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    top_emotion = sorted_preds[0][0]\n",
    "    top_score = sorted_preds[0][1]\n",
    "    second_emotion = sorted_preds[1][0]\n",
    "    second_score = sorted_preds[1][1]\n",
    "    margin = top_score - second_score\n",
    "    \n",
    "    # Check if we should reassign\n",
    "    if top_emotion == 'Neutral' and margin < threshold:\n",
    "        # Return second emotion in original format\n",
    "        # Find the second emotion in the emotion list to get its index\n",
    "        emotion_list = list(pred_dict.keys())\n",
    "        second_idx = emotion_list.index(second_emotion)\n",
    "        return f\"[{second_idx}, '{second_emotion}']\"\n",
    "    else:\n",
    "        # Keep original prediction (already in correct format)\n",
    "        return original_pred\n",
    "\n",
    "print(\"\\nApplying reassignment threshold of 1.7...\")\n",
    "print(\"This will reassign Neutral predictions where margin < 1.7 to the second emotion\\n\")\n",
    "\n",
    "# Apply reassignment\n",
    "df['PREDICTION_2'] = df.apply(lambda row: reassign_emotion(row, threshold=1.7), axis=1)\n",
    "\n",
    "# Count how many were reassigned\n",
    "original_neutral = (df['PREDICTION'] == \"[27, 'Neutral']\").sum()\n",
    "new_neutral = (df['PREDICTION_2'] == \"[27, 'Neutral']\").sum()\n",
    "reassigned = original_neutral - new_neutral\n",
    "\n",
    "print(f\"Original Neutral predictions: {original_neutral:,}\")\n",
    "print(f\"New Neutral predictions: {new_neutral:,}\")\n",
    "print(f\"Reassigned: {reassigned:,} ({reassigned/original_neutral*100:.1f}% of Neutral)\")\n",
    "print(f\"Total predictions: {len(df):,}\")\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE DATASET WITH NEW PREDICTIONS\n",
    "# ============================================================================\n",
    "\n",
    "output_file = 'Data/Final_InSentiment_Pred2.csv'\n",
    "df.to_csv(output_file, index=False)\n",
    "print(f\"\\n✓ Saved dataset with PREDICTION_2 column to '{output_file}'\")\n",
    "\n",
    "# ============================================================================\n",
    "# STEP 2: GENERATE EMOTION DISTRIBUTION BY SHOW\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING EMOTION DISTRIBUTION BY SHOW\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Group by SHOW and PREDICTION_2, count occurrences\n",
    "emotion_counts = df.groupby(['SHOW', 'PREDICTION_2']).size().reset_index(name='count')\n",
    "\n",
    "# Calculate total counts per show\n",
    "show_totals = df.groupby('SHOW').size().reset_index(name='total_count')\n",
    "\n",
    "# Merge to get totals\n",
    "emotion_dist = emotion_counts.merge(show_totals, on='SHOW')\n",
    "\n",
    "# Calculate percentages\n",
    "emotion_dist['PERCENTAGES'] = (emotion_dist['count'] / emotion_dist['total_count']) * 100\n",
    "\n",
    "# Drop the total_count column (we don't need it in final output)\n",
    "emotion_dist = emotion_dist[['SHOW', 'PREDICTION_2', 'count', 'PERCENTAGES']]\n",
    "\n",
    "# Rename PREDICTION_2 to PREDICTION for consistency with your format\n",
    "emotion_dist.rename(columns={'PREDICTION_2': 'PREDICTION'}, inplace=True)\n",
    "\n",
    "# Sort by SHOW and count (descending)\n",
    "emotion_dist = emotion_dist.sort_values(['SHOW', 'count'], ascending=[True, False])\n",
    "\n",
    "print(f\"\\nGenerated emotion distribution for {emotion_dist['SHOW'].nunique()} shows\")\n",
    "print(f\"Total rows: {len(emotion_dist)}\")\n",
    "\n",
    "# Display sample for first show\n",
    "first_show = emotion_dist['SHOW'].iloc[0]\n",
    "print(f\"\\nSample output for '{first_show}':\")\n",
    "print(emotion_dist[emotion_dist['SHOW'] == first_show].to_string(index=False))\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE EMOTION DISTRIBUTION\n",
    "# ============================================================================\n",
    "\n",
    "output_dist_file = 'Data/Final_InSentiment_ByShow.csv'\n",
    "emotion_dist.to_csv(output_dist_file, index=False)\n",
    "print(f\"\\n✓ Saved emotion distribution to '{output_dist_file}'\")\n",
    "\n",
    "# ============================================================================\n",
    "# COMPARISON STATISTICS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPARISON: ORIGINAL vs REASSIGNED\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Function to extract clean emotion name from original format\n",
    "def extract_emotion_name(pred_string):\n",
    "    \"\"\"\n",
    "    Extract emotion name from format like \"[27, 'Neutral']\" to just \"Neutral\"\n",
    "    \"\"\"\n",
    "    if isinstance(pred_string, str):\n",
    "        # Extract the emotion name between the quotes\n",
    "        import re\n",
    "        match = re.search(r\"'([^']+)'\", pred_string)\n",
    "        if match:\n",
    "            return match.group(1)\n",
    "    return pred_string\n",
    "\n",
    "# Create normalized version of original predictions for comparison\n",
    "df['PREDICTION_NORMALIZED'] = df['PREDICTION'].apply(extract_emotion_name)\n",
    "\n",
    "# Calculate original distribution with normalized names\n",
    "original_dist = df.groupby(['SHOW', 'PREDICTION_NORMALIZED']).size().reset_index(name='count')\n",
    "original_totals = df.groupby('SHOW').size().reset_index(name='total_count')\n",
    "original_dist = original_dist.merge(original_totals, on='SHOW')\n",
    "original_dist['PERCENTAGES'] = (original_dist['count'] / original_dist['total_count']) * 100\n",
    "\n",
    "# Calculate average neutral percentage across all shows\n",
    "original_neutral_pct = original_dist[original_dist['PREDICTION_NORMALIZED'] == 'Neutral'].groupby('SHOW')['PERCENTAGES'].sum().mean()\n",
    "new_neutral_pct = emotion_dist[emotion_dist['PREDICTION'].str.contains('Neutral', na=False)].groupby('SHOW')['PERCENTAGES'].sum().mean()\n",
    "\n",
    "print(f\"\\nAverage Neutral percentage across shows:\")\n",
    "print(f\"  Original: {original_neutral_pct:.2f}%\")\n",
    "print(f\"  Reassigned: {new_neutral_pct:.2f}%\")\n",
    "print(f\"  Difference: {original_neutral_pct - new_neutral_pct:.2f} percentage points\")\n",
    "\n",
    "# Top emotions that gained from reassignment\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "print(\"Emotions that gained the most from reassignment:\")\n",
    "print(\"-\"*80)\n",
    "\n",
    "# Normalize the new predictions too for comparison\n",
    "emotion_dist['PREDICTION_NORMALIZED'] = emotion_dist['PREDICTION'].apply(extract_emotion_name)\n",
    "\n",
    "# Compare total counts across all shows (both normalized)\n",
    "original_emotion_totals = original_dist.groupby('PREDICTION_NORMALIZED')['count'].sum().sort_values(ascending=False)\n",
    "new_emotion_totals = emotion_dist.groupby('PREDICTION_NORMALIZED')['count'].sum().sort_values(ascending=False)\n",
    "\n",
    "# Create comparison - merge on emotion name\n",
    "comparison = pd.DataFrame({\n",
    "    'original_count': original_emotion_totals,\n",
    "    'new_count': new_emotion_totals\n",
    "}).fillna(0)\n",
    "comparison['difference'] = comparison['new_count'] - comparison['original_count']\n",
    "comparison['pct_change'] = (comparison['difference'] / comparison['original_count'] * 100).replace([np.inf, -np.inf], 0)\n",
    "\n",
    "# Show top gainers (excluding Neutral which loses)\n",
    "top_gainers = comparison[comparison['difference'] > 0].sort_values('difference', ascending=False).head(10)\n",
    "print(\"\\nTop 10 emotions that gained:\")\n",
    "print(top_gainers[['original_count', 'new_count', 'difference', 'pct_change']].to_string())\n",
    "\n",
    "# Show Neutral separately (it should lose)\n",
    "if 'Neutral' in comparison.index:\n",
    "    print(\"\\nNeutral (should decrease):\")\n",
    "    neutral_row = comparison.loc[['Neutral']][['original_count', 'new_count', 'difference', 'pct_change']]\n",
    "    print(neutral_row.to_string())\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"REASSIGNMENT COMPLETE\")\n",
    "print(\"=\"*80)\n",
    "print(f\"\\nOutput files created:\")\n",
    "print(f\"  1. {output_file} - Full dataset with PREDICTION_2 column\")\n",
    "print(f\"  2. {output_dist_file} - Emotion distribution by show (ready for analysis)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32daa688-e0bf-407a-afe8-5f4b0d1f3356",
   "metadata": {},
   "source": [
    "### Calculate Emotional Diversity Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47aed03a-d099-42df-a023-c8b110688ac7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "EMOTIONAL DIVERSITY ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "Top 15 emotions (by mean % across all shows):\n",
      "   1. [0, 'Admiration']              (avg 4.39%)\n",
      "   2. [4, 'Approval']                (avg 4.37%)\n",
      "   3. [18, 'Love']                   (avg 3.69%)\n",
      "   4. [10, 'Disapproval']            (avg 3.55%)\n",
      "   5. [3, 'Annoyance']               (avg 3.36%)\n",
      "   6. [6, 'Confusion']               (avg 2.80%)\n",
      "   7. [1, 'Amusement']               (avg 2.42%)\n",
      "   8. [25, 'Sadness']                (avg 2.14%)\n",
      "   9. [9, 'Disappointment']          (avg 1.52%)\n",
      "  10. [22, 'Realization']            (avg 1.41%)\n",
      "  11. [7, 'Curiosity']               (avg 1.36%)\n",
      "  12. [2, 'Anger']                   (avg 1.26%)\n",
      "  13. [17, 'Joy']                    (avg 1.15%)\n",
      "  14. [20, 'Optimism']               (avg 0.98%)\n",
      "  15. [8, 'Desire']                  (avg 0.91%)\n",
      "\n",
      "================================================================================\n",
      "DIVERSITY METRICS FOR ALL SHOWS\n",
      "================================================================================\n",
      "\n",
      "Summary Statistics:\n",
      "       shannon_entropy_all  effective_number_all  shannon_entropy_top15  \\\n",
      "count            50.000000             50.000000              50.000000   \n",
      "mean              4.050039             16.581395               3.662072   \n",
      "std               0.065461              0.752465               0.054963   \n",
      "min               3.893194             14.858269               3.470848   \n",
      "25%               4.004918             16.054640               3.629517   \n",
      "50%               4.057859             16.654775               3.673568   \n",
      "75%               4.088358             17.010572               3.698105   \n",
      "max               4.224039             18.687980               3.758494   \n",
      "\n",
      "       effective_number_top15  \n",
      "count               50.000000  \n",
      "mean                12.667723  \n",
      "std                  0.474155  \n",
      "min                 11.087392  \n",
      "25%                 12.376435  \n",
      "50%                 12.760108  \n",
      "75%                 12.978983  \n",
      "max                 13.533788  \n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "INTERPRETATION GUIDE:\n",
      "--------------------------------------------------------------------------------\n",
      "Shannon Entropy (all emotions):\n",
      "  - Theoretical max: ~4.75 (if all 27 non-neutral emotions were equal)\n",
      "  - Higher values = more diverse emotional responses\n",
      "  - Lower values = emotions concentrated in fewer categories\n",
      "\n",
      "Effective Number of Emotions:\n",
      "  - Represents 'how many equally-common emotions' would give same diversity\n",
      "  - E.g., value of 8.5 = equivalent to ~8-9 equally prevalent emotions\n",
      "  - More intuitive than raw entropy\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (All Emotions)\n",
      "================================================================================\n",
      "                           SHOW  total_comments  n_emotions_present  shannon_entropy_all  effective_number_all\n",
      "            The Handmaid's Tale          152182                  26             4.224039             18.687980\n",
      "               Better Call Saul          113205                  26             4.176634             18.083900\n",
      "Marvel's Agents Of S.H.I.E.L.D.           32426                  25             4.161367             17.893539\n",
      "                       Euphoria          116428                  26             4.129351             17.500826\n",
      "                Stranger Things           82115                  25             4.125957             17.459696\n",
      "                        Lucifer           31426                  25             4.123942             17.435329\n",
      "                Attack On Titan          199773                  26             4.117092             17.352742\n",
      "                 13 Reasons Why           32397                  26             4.107806             17.241408\n",
      "                      Riverdale           23704                  24             4.095384             17.093593\n",
      "                The Mandalorian           11705                  24             4.094923             17.088139\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH LOWEST EMOTIONAL DIVERSITY (All Emotions)\n",
      "================================================================================\n",
      "                SHOW  total_comments  n_emotions_present  shannon_entropy_all  effective_number_all\n",
      "Crash Landing on You            3175                  25             3.893194             14.858269\n",
      "          PAW Patrol            4398                  22             3.910729             15.039966\n",
      "  Brooklyn Nine-Nine           33615                  26             3.946950             15.422342\n",
      "                Dark           47624                  26             3.957139             15.531643\n",
      " The Big Bang Theory           13081                  26             3.964555             15.611688\n",
      "           The Flash           37789                  26             3.966832             15.636352\n",
      "             Vikings           28195                  26             3.978304             15.761182\n",
      "    My Hero Academia           46100                  25             3.978569             15.764080\n",
      "      Grey's Anatomy           43410                  25             3.982840             15.810815\n",
      "      Jujutsu Kaisen           15517                  25             3.991727             15.908510\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (Top 15 Emotions)\n",
      "================================================================================\n",
      "                           SHOW  total_comments  shannon_entropy_top15  effective_number_top15\n",
      "            The Handmaid's Tale          152182               3.758494               13.533788\n",
      "                        Lucifer           31426               3.738694               13.349315\n",
      "                     Sweet Home            2226               3.736806               13.331854\n",
      "Marvel's Agents Of S.H.I.E.L.D.           32426               3.736269               13.326900\n",
      "        Orange Is The New Black           10389               3.722598               13.201210\n",
      "                Stranger Things           82115               3.722147               13.197082\n",
      "               Better Call Saul          113205               3.716107               13.141945\n",
      "                Attack On Titan          199773               3.714347               13.125921\n",
      "                The Mandalorian           11705               3.709612               13.082915\n",
      "                Game Of Thrones          248134               3.706978               13.059053\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH LOWEST EMOTIONAL DIVERSITY (Top 15 Emotions)\n",
      "================================================================================\n",
      "                SHOW  total_comments  shannon_entropy_top15  effective_number_top15\n",
      "Crash Landing on You            3175               3.470848               11.087392\n",
      "  Brooklyn Nine-Nine           33615               3.562789               11.816973\n",
      " The Big Bang Theory           13081               3.566864               11.850398\n",
      "          PAW Patrol            4398               3.573967               11.908892\n",
      "                Dark           47624               3.576023               11.925871\n",
      "    My Hero Academia           46100               3.581411               11.970497\n",
      "           Outlander           33495               3.603111               12.151911\n",
      "             Vikings           28195               3.612313               12.229667\n",
      "   The Wheel Of Time           30792               3.621432               12.307210\n",
      "           The Flash           37789               3.623706               12.326627\n",
      "\n",
      "================================================================================\n",
      "COMPLETE DIVERSITY METRICS (sorted by Shannon Entropy All)\n",
      "================================================================================\n",
      "                             SHOW  total_comments  non_neutral_comments  n_emotions_present  shannon_entropy_all  effective_number_all  shannon_entropy_top15  effective_number_top15\n",
      "              The Handmaid's Tale          152182                 57619                  26             4.224039             18.687980               3.758494               13.533788\n",
      "                 Better Call Saul          113205                 39543                  26             4.176634             18.083900               3.716107               13.141945\n",
      "  Marvel's Agents Of S.H.I.E.L.D.           32426                 10190                  25             4.161367             17.893539               3.736269               13.326900\n",
      "                         Euphoria          116428                 51031                  26             4.129351             17.500826               3.688723               12.894851\n",
      "                  Stranger Things           82115                 35012                  25             4.125957             17.459696               3.722147               13.197082\n",
      "                          Lucifer           31426                 12011                  25             4.123942             17.435329               3.738694               13.349315\n",
      "                  Attack On Titan          199773                 70740                  26             4.117092             17.352742               3.714347               13.125921\n",
      "                   13 Reasons Why           32397                 14522                  26             4.107806             17.241408               3.672209               12.748089\n",
      "                        Riverdale           23704                  9312                  24             4.095384             17.093593               3.698412               12.981743\n",
      "                  The Mandalorian           11705                  4147                  24             4.094923             17.088139               3.709612               13.082915\n",
      "                 The Walking Dead           34674                 14654                  25             4.093759             17.074355               3.696577               12.965238\n",
      "             The Umbrella Academy           66401                 27253                  26             4.092488             17.059318               3.699955               12.995637\n",
      "                  Game Of Thrones          248134                 84031                  26             4.089476             17.023733               3.706978               13.059053\n",
      "            SpongeBob SquarePants            5113                  2401                  24             4.085007             16.971089               3.639897               12.465739\n",
      "          Orange Is The New Black           10389                  5493                  26             4.079658             16.908279               3.722598               13.201210\n",
      "                       Bridgerton           54321                 22684                  25             4.079380             16.905023               3.680356               12.820278\n",
      "                        Cobra Kai           81664                 28155                  26             4.079135             16.902151               3.664303               12.678422\n",
      "                      WandaVision           26423                  8305                  25             4.078117             16.890224               3.681830               12.833384\n",
      "                         The Boys           67636                 25413                  25             4.077306             16.880738               3.649881               12.552311\n",
      "                      The Witcher          120505                 43484                  25             4.075372             16.858121               3.655059               12.597447\n",
      "                   Rick And Morty           51377                 19051                  25             4.074277             16.845335               3.696198               12.961837\n",
      "                          The 100           59007                 24334                  26             4.071335             16.811012               3.697185               12.970704\n",
      "                       Sweet Home            2226                   902                  24             4.069005             16.783892               3.736806               13.331854\n",
      "                        Westworld           46744                 14602                  26             4.065950             16.748386               3.690469               12.910462\n",
      "                     Supernatural           36205                 14887                  26             4.061813             16.700423               3.686605               12.875928\n",
      "                       Squid Game           19267                  7839                  26             4.053904             16.609127               3.645693               12.515927\n",
      "            American Horror Story            6189                  3034                  25             4.053764             16.607512               3.643530               12.497172\n",
      "            The Book Of Boba Fett           19770                  7476                  25             4.043962             16.495063               3.703658               13.029034\n",
      "                      Moon Knight           10395                  3327                  24             4.042905             16.482976               3.661275               12.651837\n",
      "                             Loki           22926                  7659                  25             4.039954             16.449292               3.660359               12.643810\n",
      "                 La Casa De Papel           15461                  6444                  26             4.035895             16.403081               3.675400               12.776315\n",
      "                   Peaky Blinders           16378                  7432                  25             4.035683             16.400667               3.687711               12.885805\n",
      "The Falcon And The Winter Soldier           21222                  7729                  25             4.032011             16.358983               3.674927               12.772127\n",
      "                Dragon Ball Super           73096                 24021                  25             4.020804             16.232391               3.637374               12.443961\n",
      "                          Hawkeye            7478                  2803                  24             4.019813             16.221250               3.678855               12.806954\n",
      "                        One Piece          204292                 66138                  26             4.015578             16.173706               3.625208               12.339467\n",
      "                        Outlander           33495                 14594                  26             4.005159             16.057321               3.603111               12.151911\n",
      "                The Wheel Of Time           30792                 10923                  24             4.004838             16.053747               3.621432               12.307210\n",
      "                            Arrow           22314                  8598                  24             3.999568             15.995214               3.648916               12.543920\n",
      "                       South Park            5894                  2778                  24             3.998679             15.985353               3.655497               12.601265\n",
      "                   Jujutsu Kaisen           15517                  5913                  25             3.991727             15.908510               3.626898               12.353927\n",
      "                   Grey's Anatomy           43410                 20882                  25             3.982840             15.810815               3.626102               12.347118\n",
      "                 My Hero Academia           46100                 17832                  25             3.978569             15.764080               3.581411               11.970497\n",
      "                          Vikings           28195                 12081                  26             3.978304             15.761182               3.612313               12.229667\n",
      "                        The Flash           37789                 13866                  26             3.966832             15.636352               3.623706               12.326627\n",
      "              The Big Bang Theory           13081                  5798                  26             3.964555             15.611688               3.566864               11.850398\n",
      "                             Dark           47624                 13609                  26             3.957139             15.531643               3.576023               11.925871\n",
      "               Brooklyn Nine-Nine           33615                 15422                  26             3.946950             15.422342               3.562789               11.816973\n",
      "                       PAW Patrol            4398                  1708                  22             3.910729             15.039966               3.573967               11.908892\n",
      "             Crash Landing on You            3175                  1303                  25             3.893194             14.858269               3.470848               11.087392\n",
      "\n",
      "================================================================================\n",
      "RELATIONSHIP BETWEEN SAMPLE SIZE AND DIVERSITY\n",
      "================================================================================\n",
      "\n",
      "Correlation between total comments and Shannon entropy (all emotions): 0.400\n",
      "Correlation between total comments and Shannon entropy (top 15): 0.257\n",
      "\n",
      "Correlation between # emotions present and Shannon entropy: 0.224\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'emotional_diversity_visualizations.png'\n",
      "\n",
      "Results saved to 'Sentiment_Data/All_emotional_diversity_results.csv'\n",
      "\n",
      "================================================================================\n",
      "ANALYSIS COMPLETE\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import entropy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load your data\n",
    "# Adjust the file path to match your file location and type\n",
    "df = pd.read_csv('Data/Final_InSentiment_ByShow.csv')  # or read_excel() if it's an Excel file\n",
    "\n",
    "# ============================================================================\n",
    "# DEFINE DIVERSITY METRICS\n",
    "# ============================================================================\n",
    "\n",
    "def calculate_shannon_entropy(show_data, exclude_neutral=True):\n",
    "    \"\"\"\n",
    "    Calculate Shannon entropy for emotion distribution.\n",
    "    Higher values indicate more diverse/spread out emotions.\n",
    "    \n",
    "    Parameters:\n",
    "    - show_data: DataFrame with emotion data for one show\n",
    "    - exclude_neutral: Whether to exclude neutral sentiment (default True)\n",
    "    \n",
    "    Returns:\n",
    "    - Shannon entropy (base 2)\n",
    "    \"\"\"\n",
    "    if exclude_neutral:\n",
    "        # Filter out neutral emotions\n",
    "        data = show_data[~show_data['PREDICTION'].str.contains('Neutral', case=False, na=False)]\n",
    "    else:\n",
    "        data = show_data\n",
    "    \n",
    "    if len(data) == 0 or data['count'].sum() == 0:\n",
    "        return np.nan\n",
    "    \n",
    "    # Get proportions (normalize to sum to 1)\n",
    "    proportions = data['count'] / data['count'].sum()\n",
    "    \n",
    "    # Calculate Shannon entropy (base 2)\n",
    "    H = entropy(proportions, base=2)\n",
    "    \n",
    "    return H\n",
    "\n",
    "\n",
    "def calculate_effective_number(show_data, exclude_neutral=True):\n",
    "    \"\"\"\n",
    "    Calculate effective number of emotions.\n",
    "    This is exp(Shannon entropy) and represents the number of \n",
    "    \"equally common\" emotions that would give the same diversity.\n",
    "    \n",
    "    More interpretable than raw entropy.\n",
    "    \"\"\"\n",
    "    H = calculate_shannon_entropy(show_data, exclude_neutral)\n",
    "    \n",
    "    if np.isnan(H):\n",
    "        return np.nan\n",
    "    \n",
    "    # Effective number = exp(entropy)\n",
    "    # Using base 2 for entropy, so we use 2^H\n",
    "    return 2 ** H\n",
    "\n",
    "\n",
    "def get_top_n_emotions_overall(df, n=15, exclude_neutral=True):\n",
    "    \"\"\"\n",
    "    Get the top N most common emotions across all shows.\n",
    "    \n",
    "    Returns:\n",
    "    - List of emotion names\n",
    "    \"\"\"\n",
    "    if exclude_neutral:\n",
    "        emotion_data = df[~df['PREDICTION'].str.contains('Neutral', case=False, na=False)]\n",
    "    else:\n",
    "        emotion_data = df\n",
    "    \n",
    "    # Calculate mean percentage across all shows for each emotion\n",
    "    top_emotions = (emotion_data.groupby('PREDICTION')['PERCENTAGES']\n",
    "                    .mean()\n",
    "                    .sort_values(ascending=False)\n",
    "                    .head(n)\n",
    "                    .index.tolist())\n",
    "    \n",
    "    return top_emotions\n",
    "\n",
    "\n",
    "# ============================================================================\n",
    "# CALCULATE DIVERSITY METRICS FOR ALL SHOWS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"EMOTIONAL DIVERSITY ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Get top 15 emotions overall\n",
    "top_15_emotions = get_top_n_emotions_overall(df, n=15, exclude_neutral=True)\n",
    "print(f\"\\nTop 15 emotions (by mean % across all shows):\")\n",
    "for i, emotion in enumerate(top_15_emotions, 1):\n",
    "    mean_pct = df[df['PREDICTION'] == emotion]['PERCENTAGES'].mean()\n",
    "    print(f\"  {i:2d}. {emotion:30s} (avg {mean_pct:.2f}%)\")\n",
    "\n",
    "# Calculate diversity metrics for each show\n",
    "diversity_results = []\n",
    "\n",
    "for show in df['SHOW'].unique():\n",
    "    show_data = df[df['SHOW'] == show]\n",
    "    \n",
    "    # Total comments\n",
    "    total_comments = show_data['count'].sum()\n",
    "    \n",
    "    # Non-neutral comments\n",
    "    non_neutral_data = show_data[~show_data['PREDICTION'].str.contains('Neutral', case=False, na=False)]\n",
    "    non_neutral_comments = non_neutral_data['count'].sum()\n",
    "    \n",
    "    # Number of distinct emotions that appear\n",
    "    n_emotions_present = len(non_neutral_data)\n",
    "    \n",
    "    # ALL EMOTIONS METRICS\n",
    "    shannon_all = calculate_shannon_entropy(show_data, exclude_neutral=True)\n",
    "    effective_all = calculate_effective_number(show_data, exclude_neutral=True)\n",
    "    \n",
    "    # TOP 15 EMOTIONS METRICS\n",
    "    top15_data = show_data[show_data['PREDICTION'].isin(top_15_emotions)]\n",
    "    shannon_top15 = calculate_shannon_entropy(top15_data, exclude_neutral=False)  # Already filtered\n",
    "    effective_top15 = calculate_effective_number(top15_data, exclude_neutral=False)\n",
    "    \n",
    "    diversity_results.append({\n",
    "        'SHOW': show,\n",
    "        'total_comments': total_comments,\n",
    "        'non_neutral_comments': non_neutral_comments,\n",
    "        'n_emotions_present': n_emotions_present,\n",
    "        'shannon_entropy_all': shannon_all,\n",
    "        'effective_number_all': effective_all,\n",
    "        'shannon_entropy_top15': shannon_top15,\n",
    "        'effective_number_top15': effective_top15\n",
    "    })\n",
    "\n",
    "diversity_df = pd.DataFrame(diversity_results).sort_values('shannon_entropy_all', ascending=False)\n",
    "\n",
    "# ============================================================================\n",
    "# DISPLAY RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"DIVERSITY METRICS FOR ALL SHOWS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nSummary Statistics:\")\n",
    "print(diversity_df[['shannon_entropy_all', 'effective_number_all', \n",
    "                    'shannon_entropy_top15', 'effective_number_top15']].describe())\n",
    "\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "print(\"INTERPRETATION GUIDE:\")\n",
    "print(\"-\"*80)\n",
    "print(\"Shannon Entropy (all emotions):\")\n",
    "print(\"  - Theoretical max: ~4.75 (if all 27 non-neutral emotions were equal)\")\n",
    "print(\"  - Higher values = more diverse emotional responses\")\n",
    "print(\"  - Lower values = emotions concentrated in fewer categories\")\n",
    "print(\"\\nEffective Number of Emotions:\")\n",
    "print(\"  - Represents 'how many equally-common emotions' would give same diversity\")\n",
    "print(\"  - E.g., value of 8.5 = equivalent to ~8-9 equally prevalent emotions\")\n",
    "print(\"  - More intuitive than raw entropy\")\n",
    "print(\"-\"*80)\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (All Emotions)\")\n",
    "print(\"=\"*80)\n",
    "top_diverse = diversity_df.nlargest(10, 'effective_number_all')[\n",
    "    ['SHOW', 'total_comments', 'n_emotions_present', \n",
    "     'shannon_entropy_all', 'effective_number_all']\n",
    "]\n",
    "print(top_diverse.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH LOWEST EMOTIONAL DIVERSITY (All Emotions)\")\n",
    "print(\"=\"*80)\n",
    "bottom_diverse = diversity_df.nsmallest(10, 'effective_number_all')[\n",
    "    ['SHOW', 'total_comments', 'n_emotions_present', \n",
    "     'shannon_entropy_all', 'effective_number_all']\n",
    "]\n",
    "print(bottom_diverse.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (Top 15 Emotions)\")\n",
    "print(\"=\"*80)\n",
    "top_diverse_15 = diversity_df.nlargest(10, 'effective_number_top15')[\n",
    "    ['SHOW', 'total_comments', 'shannon_entropy_top15', 'effective_number_top15']\n",
    "]\n",
    "print(top_diverse_15.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH LOWEST EMOTIONAL DIVERSITY (Top 15 Emotions)\")\n",
    "print(\"=\"*80)\n",
    "bottom_diverse_15 = diversity_df.nsmallest(10, 'effective_number_top15')[\n",
    "    ['SHOW', 'total_comments', 'shannon_entropy_top15', 'effective_number_top15']\n",
    "]\n",
    "print(bottom_diverse_15.to_string(index=False))\n",
    "\n",
    "# ============================================================================\n",
    "# FULL DATASET\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPLETE DIVERSITY METRICS (sorted by Shannon Entropy All)\")\n",
    "print(\"=\"*80)\n",
    "print(diversity_df.to_string(index=False))\n",
    "\n",
    "# ============================================================================\n",
    "# RELATIONSHIP ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"RELATIONSHIP BETWEEN SAMPLE SIZE AND DIVERSITY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Correlation between sample size and diversity\n",
    "corr_all = diversity_df['total_comments'].corr(diversity_df['shannon_entropy_all'])\n",
    "corr_top15 = diversity_df['total_comments'].corr(diversity_df['shannon_entropy_top15'])\n",
    "\n",
    "print(f\"\\nCorrelation between total comments and Shannon entropy (all emotions): {corr_all:.3f}\")\n",
    "print(f\"Correlation between total comments and Shannon entropy (top 15): {corr_top15:.3f}\")\n",
    "\n",
    "# Correlation between number of emotions present and diversity\n",
    "corr_n_emotions = diversity_df['n_emotions_present'].corr(diversity_df['shannon_entropy_all'])\n",
    "print(f\"\\nCorrelation between # emotions present and Shannon entropy: {corr_n_emotions:.3f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "\n",
    "# Plot 1: Distribution of Shannon entropy (all emotions)\n",
    "ax1 = axes[0, 0]\n",
    "diversity_df['shannon_entropy_all'].hist(bins=20, ax=ax1, edgecolor='black', color='steelblue')\n",
    "ax1.set_xlabel('Shannon Entropy (All Emotions)')\n",
    "ax1.set_ylabel('Number of Shows')\n",
    "ax1.set_title('Distribution of Emotional Diversity\\n(All Non-Neutral Emotions)')\n",
    "ax1.axvline(diversity_df['shannon_entropy_all'].mean(), color='red', \n",
    "            linestyle='--', linewidth=2, label=f'Mean: {diversity_df[\"shannon_entropy_all\"].mean():.2f}')\n",
    "ax1.axvline(diversity_df['shannon_entropy_all'].median(), color='orange', \n",
    "            linestyle='--', linewidth=2, label=f'Median: {diversity_df[\"shannon_entropy_all\"].median():.2f}')\n",
    "ax1.legend()\n",
    "\n",
    "# Plot 2: Distribution of effective number (all emotions)\n",
    "ax2 = axes[0, 1]\n",
    "diversity_df['effective_number_all'].hist(bins=20, ax=ax2, edgecolor='black', color='coral')\n",
    "ax2.set_xlabel('Effective Number of Emotions')\n",
    "ax2.set_ylabel('Number of Shows')\n",
    "ax2.set_title('Distribution of Effective Number of Emotions\\n(All Non-Neutral Emotions)')\n",
    "ax2.axvline(diversity_df['effective_number_all'].mean(), color='red', \n",
    "            linestyle='--', linewidth=2, label=f'Mean: {diversity_df[\"effective_number_all\"].mean():.2f}')\n",
    "ax2.axvline(diversity_df['effective_number_all'].median(), color='orange', \n",
    "            linestyle='--', linewidth=2, label=f'Median: {diversity_df[\"effective_number_all\"].median():.2f}')\n",
    "ax2.legend()\n",
    "\n",
    "# Plot 3: Sample size vs diversity (all emotions)\n",
    "ax3 = axes[1, 0]\n",
    "ax3.scatter(diversity_df['total_comments'], diversity_df['shannon_entropy_all'], \n",
    "            alpha=0.6, s=50)\n",
    "ax3.set_xlabel('Total Comments (log scale)')\n",
    "ax3.set_ylabel('Shannon Entropy (All Emotions)')\n",
    "ax3.set_title('Sample Size vs Emotional Diversity')\n",
    "ax3.set_xscale('log')\n",
    "# Add trend line\n",
    "z = np.polyfit(np.log10(diversity_df['total_comments']), \n",
    "               diversity_df['shannon_entropy_all'], 1)\n",
    "p = np.poly1d(z)\n",
    "x_trend = np.logspace(np.log10(diversity_df['total_comments'].min()), \n",
    "                      np.log10(diversity_df['total_comments'].max()), 100)\n",
    "ax3.plot(x_trend, p(np.log10(x_trend)), \"r--\", alpha=0.8, \n",
    "         label=f'r = {corr_all:.3f}')\n",
    "ax3.legend()\n",
    "\n",
    "# Plot 4: Comparison of all emotions vs top 15\n",
    "ax4 = axes[1, 1]\n",
    "ax4.scatter(diversity_df['effective_number_all'], \n",
    "            diversity_df['effective_number_top15'], \n",
    "            alpha=0.6, s=50, color='purple')\n",
    "ax4.set_xlabel('Effective Number (All Emotions)')\n",
    "ax4.set_ylabel('Effective Number (Top 15 Emotions)')\n",
    "ax4.set_title('Diversity: All Emotions vs Top 15 Only')\n",
    "# Add diagonal line\n",
    "max_val = max(diversity_df['effective_number_all'].max(), \n",
    "              diversity_df['effective_number_top15'].max())\n",
    "ax4.plot([0, max_val], [0, max_val], 'k--', alpha=0.3, label='y=x')\n",
    "ax4.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Sentiment_Data/All_emotional_diversity_visualizations.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'emotional_diversity_visualizations.png'\")\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE RESULTS TO CSV\n",
    "# ============================================================================\n",
    "\n",
    "output_file = 'Data/All_emotional_diversity_results.csv'\n",
    "diversity_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nResults saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295f8cf2-d219-4008-9836-92db4bd2fce4",
   "metadata": {},
   "source": [
    "### Outlier Analysis (Using correlation between sample size and diversity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67b62188-47f1-490c-a6a1-f6145b067eb5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "OUTLIER ANALYSIS: SAMPLE SIZE vs EMOTIONAL DIVERSITY\n",
      "================================================================================\n",
      "\n",
      "Focusing on TOP 15 EMOTIONS for reliable outlier detection\n",
      "(Rare emotions excluded due to high variance)\n",
      "\n",
      "================================================================================\n",
      "OUTLIER DEFINITIONS\n",
      "================================================================================\n",
      "\n",
      "Residual = Actual diversity - Predicted diversity (based on sample size)\n",
      "  Positive residual = MORE diverse than expected for its sample size\n",
      "  Negative residual = LESS diverse than expected for its sample size\n",
      "\n",
      "Z-score thresholds:\n",
      "  |z| > 2.0 = Notable outlier (top/bottom ~5%)\n",
      "  |z| > 1.5 = Moderate outlier (top/bottom ~13%)\n",
      "\n",
      "================================================================================\n",
      "OUTLIERS: MUCH MORE DIVERSE THAN EXPECTED (Top 15 Emotions, z > 2.0)\n",
      "================================================================================\n",
      "These shows have unusually diverse emotional responses for their sample size\n",
      "(Based on top 15 most reliable emotions)\n",
      "\n",
      "      SHOW  total_comments  shannon_entropy_top15  predicted_diversity_top15  residual_top15  residual_zscore_top15\n",
      "Sweet Home            2226               3.736806                   3.625077        0.111729               2.118349\n",
      "\n",
      "================================================================================\n",
      "OUTLIERS: MUCH LESS DIVERSE THAN EXPECTED (Top 15 Emotions, z < -2.0)\n",
      "================================================================================\n",
      "These shows have unusually concentrated emotional responses for their sample size\n",
      "(Based on top 15 most reliable emotions)\n",
      "\n",
      "                SHOW  total_comments  shannon_entropy_top15  predicted_diversity_top15  residual_top15  residual_zscore_top15\n",
      "Crash Landing on You            3175               3.470848                   3.630147       -0.159299              -3.020265\n",
      "\n",
      "================================================================================\n",
      "COMPARISON: TOP 15 vs ALL EMOTIONS OUTLIER DETECTION\n",
      "================================================================================\n",
      "\n",
      "Outliers detected (|z| > 1.5):\n",
      "  Using top 15 emotions: 6 shows\n",
      "  Using all emotions: 8 shows\n",
      "  In both: 3 shows\n",
      "  Only in top 15: 3 shows\n",
      "  Only in all: 5 shows\n",
      "\n",
      "Shows that are outliers with 'all emotions' but NOT with top 15:\n",
      "(These may be outliers due to rare emotion noise)\n",
      "  - Better Call Saul: z_all=1.55, z_top15=0.66\n",
      "  - Marvel's Agents Of S.H.I.E.L.D.: z_all=1.85, z_top15=1.38\n",
      "  - The Flash: z_all=-1.52, z_top15=-0.79\n",
      "  - The Handmaid's Tale: z_all=2.22, z_top15=1.39\n",
      "  - PAW Patrol: z_all=-1.51, z_top15=-1.15\n",
      "\n",
      "================================================================================\n",
      "SUMMARY OF OUTLIER CATEGORIES (based on Top 15)\n",
      "================================================================================\n",
      "\n",
      "Distribution of shows by outlier category:\n",
      "outlier_category\n",
      "Less diverse          4\n",
      "Much LESS diverse     1\n",
      "Much MORE diverse     1\n",
      "Normal               44\n",
      "Name: count, dtype: int64\n",
      "\n",
      "================================================================================\n",
      "INTERESTING PATTERNS (based on Top 15)\n",
      "================================================================================\n",
      "\n",
      "Small sample size BUT high diversity:\n",
      "(Fewer comments than median, but more diverse than median)\n",
      "\n",
      "                   SHOW  total_comments  effective_number_top15  residual_zscore_top15\n",
      "                Lucifer           31426               13.349315               1.437539\n",
      "             Sweet Home            2226               13.331854               2.118349\n",
      "Orange Is The New Black           10389               13.201210               1.431984\n",
      "        The Mandalorian           11705               13.082915               1.153487\n",
      "  The Book Of Boba Fett           19770               13.029034               0.898726\n",
      "              Riverdale           23704               12.981743               0.750140\n",
      "         Peaky Blinders           16378               12.885805               0.647313\n",
      "            WandaVision           26423               12.833384               0.406346\n",
      "                Hawkeye            7478               12.806954               0.691627\n",
      "       La Casa De Papel           15461               12.776315               0.429498\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Large sample size BUT low diversity:\n",
      "(More comments than median, but less diverse than median)\n",
      "\n",
      "              SHOW  total_comments  effective_number_top15  residual_zscore_top15\n",
      "Brooklyn Nine-Nine           33615               11.816973              -1.915812\n",
      "              Dark           47624               11.925871              -1.759192\n",
      "  My Hero Academia           46100               11.970497              -1.648225\n",
      "         Outlander           33495               12.151911              -1.150335\n",
      "         The Flash           37789               12.326627              -0.792511\n",
      "         One Piece          204292               12.339467              -1.220816\n",
      "    Grey's Anatomy           43410               12.347118              -0.784614\n",
      " Dragon Ball Super           73096               12.443961              -0.711958\n",
      "          The Boys           67636               12.552311              -0.453810\n",
      "       The Witcher          120505               12.597447              -0.511960\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'diversity_outliers_top15_analysis.png'\n",
      "Saving residual outliers plot as separate image...\n",
      "Standalone residual plot saved as 'diversity_outliers_residual_plot.png'\n",
      "\n",
      "Extended results (with residuals and outlier categories) saved to 'Sentiment_Data/diversity_with_outliers_top15.csv'\n",
      "\n",
      "================================================================================\n",
      "OUTLIER ANALYSIS COMPLETE\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Load the diversity results from previous analysis\n",
    "diversity_df = pd.read_csv('Data/All_emotional_diversity_results.csv')\n",
    "\n",
    "# ============================================================================\n",
    "# CALCULATE RESIDUALS FROM EXPECTED RELATIONSHIP\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS: SAMPLE SIZE vs EMOTIONAL DIVERSITY\")\n",
    "print(\"=\"*80)\n",
    "print(\"\\nFocusing on TOP 15 EMOTIONS for reliable outlier detection\")\n",
    "print(\"(Rare emotions excluded due to high variance)\")\n",
    "\n",
    "# Calculate regression line: diversity ~ log(sample_size)\n",
    "# Using log because the relationship appears nonlinear\n",
    "diversity_df['log_comments'] = np.log10(diversity_df['total_comments'])\n",
    "\n",
    "# PRIMARY ANALYSIS: Top 15 emotions (most reliable)\n",
    "slope_top15, intercept_top15, r_top15, p_top15, se_top15 = stats.linregress(\n",
    "    diversity_df['log_comments'], \n",
    "    diversity_df['shannon_entropy_top15']\n",
    ")\n",
    "\n",
    "diversity_df['predicted_diversity_top15'] = (\n",
    "    slope_top15 * diversity_df['log_comments'] + intercept_top15\n",
    ")\n",
    "\n",
    "diversity_df['residual_top15'] = (\n",
    "    diversity_df['shannon_entropy_top15'] - diversity_df['predicted_diversity_top15']\n",
    ")\n",
    "\n",
    "diversity_df['residual_zscore_top15'] = (\n",
    "    (diversity_df['residual_top15'] - diversity_df['residual_top15'].mean()) / \n",
    "    diversity_df['residual_top15'].std()\n",
    ")\n",
    "\n",
    "# SECONDARY ANALYSIS: All emotions (for comparison)\n",
    "slope_all, intercept_all, r_all, p_all, se_all = stats.linregress(\n",
    "    diversity_df['log_comments'], \n",
    "    diversity_df['shannon_entropy_all']\n",
    ")\n",
    "\n",
    "diversity_df['predicted_diversity_all'] = (\n",
    "    slope_all * diversity_df['log_comments'] + intercept_all\n",
    ")\n",
    "\n",
    "diversity_df['residual_all'] = (\n",
    "    diversity_df['shannon_entropy_all'] - diversity_df['predicted_diversity_all']\n",
    ")\n",
    "\n",
    "diversity_df['residual_zscore_all'] = (\n",
    "    (diversity_df['residual_all'] - diversity_df['residual_all'].mean()) / \n",
    "    diversity_df['residual_all'].std()\n",
    ")\n",
    "\n",
    "# ============================================================================\n",
    "# IDENTIFY OUTLIERS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIER DEFINITIONS\")\n",
    "print(\"=\"*80)\n",
    "print(\"\\nResidual = Actual diversity - Predicted diversity (based on sample size)\")\n",
    "print(\"  Positive residual = MORE diverse than expected for its sample size\")\n",
    "print(\"  Negative residual = LESS diverse than expected for its sample size\")\n",
    "print(\"\\nZ-score thresholds:\")\n",
    "print(\"  |z| > 2.0 = Notable outlier (top/bottom ~5%)\")\n",
    "print(\"  |z| > 1.5 = Moderate outlier (top/bottom ~13%)\")\n",
    "\n",
    "# Define outlier thresholds\n",
    "threshold_strong = 2.0\n",
    "threshold_moderate = 1.5\n",
    "\n",
    "# Categorize outliers based on TOP 15 (primary analysis)\n",
    "diversity_df['outlier_category'] = 'Normal'\n",
    "diversity_df.loc[diversity_df['residual_zscore_top15'] > threshold_strong, 'outlier_category'] = 'Much MORE diverse'\n",
    "diversity_df.loc[(diversity_df['residual_zscore_top15'] > threshold_moderate) & \n",
    "                 (diversity_df['residual_zscore_top15'] <= threshold_strong), 'outlier_category'] = 'More diverse'\n",
    "diversity_df.loc[diversity_df['residual_zscore_top15'] < -threshold_strong, 'outlier_category'] = 'Much LESS diverse'\n",
    "diversity_df.loc[(diversity_df['residual_zscore_top15'] < -threshold_moderate) & \n",
    "                 (diversity_df['residual_zscore_top15'] >= -threshold_strong), 'outlier_category'] = 'Less diverse'\n",
    "\n",
    "# ============================================================================\n",
    "# DISPLAY OUTLIERS - TOP 15 EMOTIONS (PRIMARY)\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIERS: MUCH MORE DIVERSE THAN EXPECTED (Top 15 Emotions, z > 2.0)\")\n",
    "print(\"=\"*80)\n",
    "print(\"These shows have unusually diverse emotional responses for their sample size\")\n",
    "print(\"(Based on top 15 most reliable emotions)\\n\")\n",
    "\n",
    "high_outliers = diversity_df[diversity_df['residual_zscore_top15'] > threshold_strong].sort_values(\n",
    "    'residual_zscore_top15', ascending=False\n",
    ")\n",
    "\n",
    "if len(high_outliers) > 0:\n",
    "    print(high_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                          'predicted_diversity_top15', 'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows exceed z > 2.0 threshold\")\n",
    "    print(\"\\nMost diverse relative to size (z > 1.5):\")\n",
    "    high_outliers_moderate = diversity_df[diversity_df['residual_zscore_top15'] > threshold_moderate].sort_values(\n",
    "        'residual_zscore_top15', ascending=False\n",
    "    )\n",
    "    if len(high_outliers_moderate) > 0:\n",
    "        print(high_outliers_moderate[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                                       'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"No outliers found at z > 1.5 threshold either\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIERS: MUCH LESS DIVERSE THAN EXPECTED (Top 15 Emotions, z < -2.0)\")\n",
    "print(\"=\"*80)\n",
    "print(\"These shows have unusually concentrated emotional responses for their sample size\")\n",
    "print(\"(Based on top 15 most reliable emotions)\\n\")\n",
    "\n",
    "low_outliers = diversity_df[diversity_df['residual_zscore_top15'] < -threshold_strong].sort_values(\n",
    "    'residual_zscore_top15'\n",
    ")\n",
    "\n",
    "if len(low_outliers) > 0:\n",
    "    print(low_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                         'predicted_diversity_top15', 'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows exceed z < -2.0 threshold\")\n",
    "    print(\"\\nLeast diverse relative to size (z < -1.5):\")\n",
    "    low_outliers_moderate = diversity_df[diversity_df['residual_zscore_top15'] < -threshold_moderate].sort_values(\n",
    "        'residual_zscore_top15'\n",
    "    )\n",
    "    if len(low_outliers_moderate) > 0:\n",
    "        print(low_outliers_moderate[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                                      'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"No outliers found at z < -1.5 threshold either\")\n",
    "\n",
    "# ============================================================================\n",
    "# COMPARISON: TOP 15 vs ALL EMOTIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPARISON: TOP 15 vs ALL EMOTIONS OUTLIER DETECTION\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Shows that are outliers in one but not the other\n",
    "top15_outliers = set(diversity_df[abs(diversity_df['residual_zscore_top15']) > threshold_moderate]['SHOW'])\n",
    "all_outliers = set(diversity_df[abs(diversity_df['residual_zscore_all']) > threshold_moderate]['SHOW'])\n",
    "\n",
    "only_top15 = top15_outliers - all_outliers\n",
    "only_all = all_outliers - top15_outliers\n",
    "both = top15_outliers & all_outliers\n",
    "\n",
    "print(f\"\\nOutliers detected (|z| > 1.5):\")\n",
    "print(f\"  Using top 15 emotions: {len(top15_outliers)} shows\")\n",
    "print(f\"  Using all emotions: {len(all_outliers)} shows\")\n",
    "print(f\"  In both: {len(both)} shows\")\n",
    "print(f\"  Only in top 15: {len(only_top15)} shows\")\n",
    "print(f\"  Only in all: {len(only_all)} shows\")\n",
    "\n",
    "if len(only_all) > 0:\n",
    "    print(f\"\\nShows that are outliers with 'all emotions' but NOT with top 15:\")\n",
    "    print(\"(These may be outliers due to rare emotion noise)\")\n",
    "    for show in only_all:\n",
    "        z_all = diversity_df.loc[diversity_df['SHOW'] == show, 'residual_zscore_all'].values[0]\n",
    "        z_top15 = diversity_df.loc[diversity_df['SHOW'] == show, 'residual_zscore_top15'].values[0]\n",
    "        print(f\"  - {show}: z_all={z_all:.2f}, z_top15={z_top15:.2f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# GENRE/TYPE ANALYSIS OF OUTLIERS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SUMMARY OF OUTLIER CATEGORIES (based on Top 15)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "outlier_summary = diversity_df['outlier_category'].value_counts().sort_index()\n",
    "print(\"\\nDistribution of shows by outlier category:\")\n",
    "print(outlier_summary)\n",
    "\n",
    "# ============================================================================\n",
    "# INTERESTING CASES: SPECIFIC PATTERNS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"INTERESTING PATTERNS (based on Top 15)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Small sample, high diversity\n",
    "small_diverse = diversity_df[\n",
    "    (diversity_df['total_comments'] < diversity_df['total_comments'].median()) &\n",
    "    (diversity_df['shannon_entropy_top15'] > diversity_df['shannon_entropy_top15'].median())\n",
    "].sort_values('effective_number_top15', ascending=False)\n",
    "\n",
    "print(\"\\nSmall sample size BUT high diversity:\")\n",
    "print(\"(Fewer comments than median, but more diverse than median)\\n\")\n",
    "if len(small_diverse) > 0:\n",
    "    print(small_diverse[['SHOW', 'total_comments', 'effective_number_top15', \n",
    "                         'residual_zscore_top15']].head(10).to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows match this pattern\")\n",
    "\n",
    "# Large sample, low diversity\n",
    "large_concentrated = diversity_df[\n",
    "    (diversity_df['total_comments'] > diversity_df['total_comments'].median()) &\n",
    "    (diversity_df['shannon_entropy_top15'] < diversity_df['shannon_entropy_top15'].median())\n",
    "].sort_values('effective_number_top15')\n",
    "\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "print(\"\\nLarge sample size BUT low diversity:\")\n",
    "print(\"(More comments than median, but less diverse than median)\\n\")\n",
    "if len(large_concentrated) > 0:\n",
    "    print(large_concentrated[['SHOW', 'total_comments', 'effective_number_top15', \n",
    "                              'residual_zscore_top15']].head(10).to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows match this pattern\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "\n",
    "# Plot 1: Residual plot with outliers highlighted (TOP 15)\n",
    "ax1 = axes[0, 0]\n",
    "colors = {'Normal': 'gray', 'More diverse': 'lightblue', 'Much MORE diverse': 'darkblue',\n",
    "          'Less diverse': 'lightcoral', 'Much LESS diverse': 'darkred'}\n",
    "for category in diversity_df['outlier_category'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category'] == category]\n",
    "    ax1.scatter(subset['total_comments'], subset['residual_top15'], \n",
    "               label=category, alpha=0.7, s=60, color=colors.get(category, 'gray'))\n",
    "\n",
    "ax1.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.5)\n",
    "ax1.axhline(threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='blue', linestyle=':', alpha=0.5)\n",
    "ax1.axhline(-threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='red', linestyle=':', alpha=0.5)\n",
    "ax1.set_xlabel('Total Comments (log scale)')\n",
    "ax1.set_ylabel('Residual (Actual - Predicted Diversity)')\n",
    "ax1.set_title('Outliers: Diversity vs Sample Size\\n(Top 15 Emotions)')\n",
    "ax1.set_xscale('log')\n",
    "ax1.legend(loc='best', fontsize=8)\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Add labels for outliers (moderate and strong) and Squid Game\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    # Label all outliers (Much MORE/LESS and More/Less diverse)\n",
    "    if row['outlier_category'] != 'Normal':\n",
    "        ax1.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=7, alpha=0.8)\n",
    "    # Also label Squid Game if it's not already labeled\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax1.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=8, alpha=0.9, fontweight='bold')\n",
    "\n",
    "# Plot 2: Distribution of residuals (TOP 15)\n",
    "ax2 = axes[0, 1]\n",
    "ax2.hist(diversity_df['residual_top15'], bins=20, edgecolor='black', alpha=0.7, color='steelblue')\n",
    "ax2.axvline(0, color='black', linestyle='--', linewidth=2)\n",
    "ax2.axvline(threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='blue', linestyle=':', linewidth=2, label='±1.5 SD')\n",
    "ax2.axvline(-threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='red', linestyle=':', linewidth=2)\n",
    "ax2.set_xlabel('Residual (Actual - Predicted)')\n",
    "ax2.set_ylabel('Number of Shows')\n",
    "ax2.set_title('Distribution of Residuals\\n(Top 15 Emotions)')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 3: Actual vs Predicted with regression line (TOP 15)\n",
    "ax3 = axes[0, 2]\n",
    "ax3.scatter(diversity_df['log_comments'], diversity_df['shannon_entropy_top15'], \n",
    "           alpha=0.6, s=50, label='Actual', color='steelblue')\n",
    "# Regression line\n",
    "x_line = np.linspace(diversity_df['log_comments'].min(), \n",
    "                     diversity_df['log_comments'].max(), 100)\n",
    "y_line = slope_top15 * x_line + intercept_top15\n",
    "ax3.plot(x_line, y_line, 'r--', linewidth=2, \n",
    "        label=f'Predicted (r={r_top15:.3f})')\n",
    "ax3.set_xlabel('Log10(Total Comments)')\n",
    "ax3.set_ylabel('Shannon Entropy (Top 15 Emotions)')\n",
    "ax3.set_title('Actual vs Predicted Diversity\\n(Top 15 Emotions)')\n",
    "ax3.legend()\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Highlight outliers\n",
    "outlier_mask = abs(diversity_df['residual_zscore_top15']) > threshold_moderate\n",
    "ax3.scatter(diversity_df.loc[outlier_mask, 'log_comments'],\n",
    "           diversity_df.loc[outlier_mask, 'shannon_entropy_top15'],\n",
    "           s=100, facecolors='none', edgecolors='red', linewidths=2,\n",
    "           label='Outliers', zorder=5)\n",
    "\n",
    "# Plot 4: Q-Q plot for residuals (TOP 15)\n",
    "ax4 = axes[1, 0]\n",
    "stats.probplot(diversity_df['residual_top15'], dist=\"norm\", plot=ax4)\n",
    "ax4.set_title('Q-Q Plot: Normality of Residuals\\n(Top 15 Emotions)')\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 5: Comparison of z-scores (Top 15 vs All)\n",
    "ax5 = axes[1, 1]\n",
    "ax5.scatter(diversity_df['residual_zscore_all'], diversity_df['residual_zscore_top15'], \n",
    "           alpha=0.6, s=50)\n",
    "ax5.plot([-3, 3], [-3, 3], 'k--', alpha=0.3, label='y=x')\n",
    "ax5.axhline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax5.axvline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax5.set_xlabel('Z-score (All Emotions)')\n",
    "ax5.set_ylabel('Z-score (Top 15 Emotions)')\n",
    "ax5.set_title('Comparison: Top 15 vs All Emotions\\nOutlier Detection')\n",
    "ax5.legend()\n",
    "ax5.grid(True, alpha=0.3)\n",
    "\n",
    "# Highlight shows that differ significantly\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    z_diff = abs(row['residual_zscore_all'] - row['residual_zscore_top15'])\n",
    "    if z_diff > 1.0:  # Significant difference\n",
    "        ax5.annotate(row['SHOW'], \n",
    "                    (row['residual_zscore_all'], row['residual_zscore_top15']),\n",
    "                    fontsize=6, alpha=0.7)\n",
    "\n",
    "# Plot 6: Correlation between top 15 and all emotions diversity\n",
    "ax6 = axes[1, 2]\n",
    "ax6.scatter(diversity_df['shannon_entropy_all'], diversity_df['shannon_entropy_top15'],\n",
    "           alpha=0.6, s=50, color='purple')\n",
    "corr = diversity_df['shannon_entropy_all'].corr(diversity_df['shannon_entropy_top15'])\n",
    "ax6.set_xlabel('Shannon Entropy (All Emotions)')\n",
    "ax6.set_ylabel('Shannon Entropy (Top 15 Emotions)')\n",
    "ax6.set_title(f'Diversity: All vs Top 15 Emotions\\n(correlation: {corr:.3f})')\n",
    "ax6.grid(True, alpha=0.3)\n",
    "\n",
    "# Add diagonal\n",
    "min_val = min(diversity_df['shannon_entropy_all'].min(), diversity_df['shannon_entropy_top15'].min())\n",
    "max_val = max(diversity_df['shannon_entropy_all'].max(), diversity_df['shannon_entropy_top15'].max())\n",
    "ax6.plot([min_val, max_val], [min_val, max_val], 'k--', alpha=0.3, label='y=x')\n",
    "ax6.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Sentiment_Data/diversity_outliers_top15_analysis.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'diversity_outliers_top15_analysis.png'\")\n",
    "\n",
    "# Save Plot 1 separately as a standalone image\n",
    "print(\"Saving residual outliers plot as separate image...\")\n",
    "fig_standalone = plt.figure(figsize=(8, 6))\n",
    "ax_standalone = fig_standalone.add_subplot(111)\n",
    "\n",
    "# Recreate plot 1 with same styling\n",
    "for category in diversity_df['outlier_category'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category'] == category]\n",
    "    ax_standalone.scatter(subset['total_comments'], subset['residual_top15'], \n",
    "               label=category, alpha=0.7, s=80, color=colors.get(category, 'gray'))\n",
    "\n",
    "ax_standalone.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.5)\n",
    "ax_standalone.axhline(threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='blue', linestyle=':', alpha=0.5)\n",
    "ax_standalone.axhline(-threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='red', linestyle=':', alpha=0.5)\n",
    "ax_standalone.set_xlabel('Total Comments (log scale)', fontsize=12)\n",
    "ax_standalone.set_ylabel('Residual (Actual - Predicted Diversity)', fontsize=12)\n",
    "ax_standalone.set_title('Emotional Diversity Outliers vs Sample Size\\n(Top 15 Emotions)', fontsize=14)\n",
    "ax_standalone.set_xscale('log')\n",
    "ax_standalone.legend(loc='best', fontsize=10)\n",
    "ax_standalone.grid(True, alpha=0.3)\n",
    "\n",
    "# Add labels for outliers and Squid Game\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    # Label all outliers (Much MORE/LESS and More/Less diverse)\n",
    "    if row['outlier_category'] != 'Normal':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=9, alpha=0.85)\n",
    "    # Also label Squid Game if it's not already labeled\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=10, alpha=0.95, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Sentiment_Data/diversity_outliers_residual_plot.png', dpi=300, bbox_inches='tight')\n",
    "print(\"Standalone residual plot saved as 'diversity_outliers_residual_plot.png'\")\n",
    "plt.close(fig_standalone)\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE EXTENDED RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "output_file = 'Data/diversity_with_outliers_top15.csv'\n",
    "diversity_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nExtended results (with residuals and outlier categories) saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e7baf2-0d40-4e89-8405-92a6959b4501",
   "metadata": {},
   "source": [
    "### Outlier Analysis (Using bin based approach) -- This code used to produce Figure 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7948ca7b-3fce-4d38-b1ad-ea03d0449815",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "OUTLIER ANALYSIS: CATEGORICAL BIN APPROACH\n",
      "================================================================================\n",
      "\n",
      "This approach identifies outliers within sample size categories\n",
      "rather than using regression. Less sensitive to correlation strength.\n",
      "\n",
      "================================================================================\n",
      "SAMPLE SIZE DISTRIBUTION\n",
      "================================================================================\n",
      "\n",
      "Summary statistics for total comments:\n",
      "count        50.000000\n",
      "mean      49761.060000\n",
      "std       54344.766436\n",
      "min        2226.000000\n",
      "25%       15732.250000\n",
      "50%       32411.500000\n",
      "75%       57835.500000\n",
      "max      248134.000000\n",
      "Name: total_comments, dtype: float64\n",
      "\n",
      "Quantile-based bins (33rd, 67th percentiles):\n",
      "  Small: 2226 - 21408\n",
      "  Medium: 21408 - 46635\n",
      "  Large: 46635 - 248134\n",
      "\n",
      "Chosen bins (quantile-based): ['Small (<21408)', 'Medium (21408-46635)', 'Large (>46635)']\n",
      "\n",
      "Shows per bin:\n",
      "size_bin\n",
      "Small (<21408)          17\n",
      "Medium (21408-46635)    16\n",
      "Large (>46635)          17\n",
      "Name: count, dtype: int64\n",
      "\n",
      "================================================================================\n",
      "CALCULATING OUTLIERS WITHIN EACH BIN\n",
      "================================================================================\n",
      "\n",
      "Diversity statistics by bin:\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Small (<21408):\n",
      "  N shows: 17\n",
      "  Mean entropy: 3.651\n",
      "  Std entropy: 0.065\n",
      "  Range: 3.471 - 3.737\n",
      "  Outliers: 0 high, 1 low\n",
      "\n",
      "Medium (21408-46635):\n",
      "  N shows: 16\n",
      "  Mean entropy: 3.653\n",
      "  Std entropy: 0.052\n",
      "  Range: 3.563 - 3.739\n",
      "  Outliers: 2 high, 1 low\n",
      "\n",
      "Large (>46635):\n",
      "  N shows: 17\n",
      "  Mean entropy: 3.681\n",
      "  Std entropy: 0.043\n",
      "  Range: 3.576 - 3.758\n",
      "  Outliers: 1 high, 1 low\n",
      "\n",
      "================================================================================\n",
      "OUTLIERS BY SIZE BIN\n",
      "================================================================================\n",
      "\n",
      "================================================================================\n",
      "BIN: Small (<21408)\n",
      "================================================================================\n",
      "\n",
      "MORE diverse than bin peers (z > 1.5):\n",
      "  None\n",
      "\n",
      "LESS diverse than bin peers (z < -1.5):\n",
      "                SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "Crash Landing on You            3175               3.470848       -2.7623               5.882353\n",
      "\n",
      "================================================================================\n",
      "BIN: Medium (21408-46635)\n",
      "================================================================================\n",
      "\n",
      "MORE diverse than bin peers (z > 1.5):\n",
      "                           SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "                        Lucifer           31426               3.738694      1.643379                 100.00\n",
      "Marvel's Agents Of S.H.I.E.L.D.           32426               3.736269      1.596791                  93.75\n",
      "\n",
      "LESS diverse than bin peers (z < -1.5):\n",
      "              SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "Brooklyn Nine-Nine           33615               3.562789     -1.736759                   6.25\n",
      "\n",
      "================================================================================\n",
      "BIN: Large (>46635)\n",
      "================================================================================\n",
      "\n",
      "MORE diverse than bin peers (z > 1.5):\n",
      "               SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "The Handmaid's Tale          152182               3.758494      1.796924                  100.0\n",
      "\n",
      "LESS diverse than bin peers (z < -1.5):\n",
      "SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "Dark           47624               3.576023     -2.440022               5.882353\n",
      "\n",
      "================================================================================\n",
      "COMPARISON: BIN vs REGRESSION APPROACH\n",
      "================================================================================\n",
      "\n",
      "Outliers detected (|z| > 1.5):\n",
      "  Bin approach: 6 shows\n",
      "  Regression approach: 6 shows\n",
      "  In both: 3 shows\n",
      "  Only in bin approach: 3 shows\n",
      "  Only in regression: 3 shows\n",
      "\n",
      "Shows that are outliers with BIN approach but NOT regression:\n",
      "  - Marvel's Agents Of S.H.I.E.L.D. (Medium (21408-46635)): z_bin=1.60, z_reg=1.38\n",
      "  - Lucifer (Medium (21408-46635)): z_bin=1.64, z_reg=1.44\n",
      "  - The Handmaid's Tale (Large (>46635)): z_bin=1.80, z_reg=1.39\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'diversity_outliers_bin_approach.png'\n",
      "Saving main outlier plot as separate image...\n",
      "Standalone bin outlier plot saved as 'diversity_outliers_bin_standalone.png'\n",
      "\n",
      "Results saved to 'Sentiment_Data/diversity_with_outliers_bin_approach.csv'\n",
      "\n",
      "================================================================================\n",
      "OUTLIER ANALYSIS COMPLETE\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Load the diversity results from previous analysis\n",
    "diversity_df = pd.read_csv('Data/All_emotional_diversity_results.csv')\n",
    "\n",
    "# ============================================================================\n",
    "# DEFINE SAMPLE SIZE BINS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS: CATEGORICAL BIN APPROACH\")\n",
    "print(\"=\"*80)\n",
    "print(\"\\nThis approach identifies outliers within sample size categories\")\n",
    "print(\"rather than using regression. Less sensitive to correlation strength.\")\n",
    "\n",
    "# Define bin edges - adjust these based on your data distribution\n",
    "# Let's look at the distribution first to choose sensible bins\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SAMPLE SIZE DISTRIBUTION\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nSummary statistics for total comments:\")\n",
    "print(diversity_df['total_comments'].describe())\n",
    "\n",
    "# Define bins based on quantiles or meaningful breaks\n",
    "# Using quantile-based bins for better balance\n",
    "quantile_bins = diversity_df['total_comments'].quantile([0, 0.33, 0.67, 1.0]).tolist()\n",
    "print(f\"\\nQuantile-based bins (33rd, 67th percentiles):\")\n",
    "print(f\"  Small: {quantile_bins[0]:.0f} - {quantile_bins[1]:.0f}\")\n",
    "print(f\"  Medium: {quantile_bins[1]:.0f} - {quantile_bins[2]:.0f}\")\n",
    "print(f\"  Large: {quantile_bins[2]:.0f} - {quantile_bins[3]:.0f}\")\n",
    "\n",
    "# Use quantile bins for better balance\n",
    "bin_edges = quantile_bins\n",
    "bin_labels = [f'Small (<{quantile_bins[1]:.0f})', \n",
    "              f'Medium ({quantile_bins[1]:.0f}-{quantile_bins[2]:.0f})', \n",
    "              f'Large (>{quantile_bins[2]:.0f})']\n",
    "\n",
    "diversity_df['size_bin'] = pd.cut(diversity_df['total_comments'], \n",
    "                                   bins=bin_edges, \n",
    "                                   labels=bin_labels,\n",
    "                                   include_lowest=True)\n",
    "\n",
    "print(f\"\\nChosen bins (quantile-based): {bin_labels}\")\n",
    "print(\"\\nShows per bin:\")\n",
    "print(diversity_df['size_bin'].value_counts().sort_index())\n",
    "\n",
    "# ============================================================================\n",
    "# CALCULATE Z-SCORES WITHIN EACH BIN\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"CALCULATING OUTLIERS WITHIN EACH BIN\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Calculate z-scores within each bin\n",
    "diversity_df['z_within_bin'] = diversity_df.groupby('size_bin', observed=True)['shannon_entropy_top15'].transform(\n",
    "    lambda x: (x - x.mean()) / x.std()\n",
    ")\n",
    "\n",
    "# Also calculate percentile ranks within each bin\n",
    "diversity_df['percentile_within_bin'] = diversity_df.groupby('size_bin', observed=True)['shannon_entropy_top15'].transform(\n",
    "    lambda x: x.rank(pct=True) * 100\n",
    ")\n",
    "\n",
    "# Define outlier thresholds\n",
    "threshold_strong = 2.0  # z-score\n",
    "threshold_moderate = 1.5\n",
    "\n",
    "# Categorize outliers\n",
    "diversity_df['outlier_category_bin'] = 'Normal'\n",
    "diversity_df.loc[diversity_df['z_within_bin'] > threshold_strong, 'outlier_category_bin'] = 'Much MORE diverse'\n",
    "diversity_df.loc[(diversity_df['z_within_bin'] > threshold_moderate) & \n",
    "                 (diversity_df['z_within_bin'] <= threshold_strong), 'outlier_category_bin'] = 'More diverse'\n",
    "diversity_df.loc[diversity_df['z_within_bin'] < -threshold_strong, 'outlier_category_bin'] = 'Much LESS diverse'\n",
    "diversity_df.loc[(diversity_df['z_within_bin'] < -threshold_moderate) & \n",
    "                 (diversity_df['z_within_bin'] >= -threshold_strong), 'outlier_category_bin'] = 'Less diverse'\n",
    "\n",
    "# ============================================================================\n",
    "# STATISTICS BY BIN\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\nDiversity statistics by bin:\")\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "\n",
    "for bin_label in bin_labels:\n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label]\n",
    "    if len(bin_data) > 0:\n",
    "        print(f\"\\n{bin_label}:\")\n",
    "        print(f\"  N shows: {len(bin_data)}\")\n",
    "        print(f\"  Mean entropy: {bin_data['shannon_entropy_top15'].mean():.3f}\")\n",
    "        print(f\"  Std entropy: {bin_data['shannon_entropy_top15'].std():.3f}\")\n",
    "        print(f\"  Range: {bin_data['shannon_entropy_top15'].min():.3f} - {bin_data['shannon_entropy_top15'].max():.3f}\")\n",
    "        \n",
    "        # Count outliers in this bin\n",
    "        n_high = (bin_data['z_within_bin'] > threshold_moderate).sum()\n",
    "        n_low = (bin_data['z_within_bin'] < -threshold_moderate).sum()\n",
    "        print(f\"  Outliers: {n_high} high, {n_low} low\")\n",
    "\n",
    "# ============================================================================\n",
    "# DISPLAY OUTLIERS BY BIN\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIERS BY SIZE BIN\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "for bin_label in bin_labels:\n",
    "    print(\"\\n\" + \"=\"*80)\n",
    "    print(f\"BIN: {bin_label}\")\n",
    "    print(\"=\"*80)\n",
    "    \n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label].copy()\n",
    "    \n",
    "    if len(bin_data) == 0:\n",
    "        print(\"No shows in this bin\")\n",
    "        continue\n",
    "    \n",
    "    # High outliers\n",
    "    high_outliers = bin_data[bin_data['z_within_bin'] > threshold_moderate].sort_values(\n",
    "        'z_within_bin', ascending=False\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nMORE diverse than bin peers (z > 1.5):\")\n",
    "    if len(high_outliers) > 0:\n",
    "        print(high_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                             'z_within_bin', 'percentile_within_bin']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"  None\")\n",
    "    \n",
    "    # Low outliers\n",
    "    low_outliers = bin_data[bin_data['z_within_bin'] < -threshold_moderate].sort_values(\n",
    "        'z_within_bin'\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nLESS diverse than bin peers (z < -1.5):\")\n",
    "    if len(low_outliers) > 0:\n",
    "        print(low_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                            'z_within_bin', 'percentile_within_bin']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"  None\")\n",
    "\n",
    "# ============================================================================\n",
    "# COMPARE TO REGRESSION APPROACH\n",
    "# ============================================================================\n",
    "\n",
    "# Load or calculate regression-based outliers for comparison\n",
    "diversity_df['log_comments'] = np.log10(diversity_df['total_comments'])\n",
    "slope, intercept, r_value, p_value, se = stats.linregress(\n",
    "    diversity_df['log_comments'], \n",
    "    diversity_df['shannon_entropy_top15']\n",
    ")\n",
    "diversity_df['predicted_diversity'] = slope * diversity_df['log_comments'] + intercept\n",
    "diversity_df['residual'] = diversity_df['shannon_entropy_top15'] - diversity_df['predicted_diversity']\n",
    "diversity_df['z_regression'] = (diversity_df['residual'] - diversity_df['residual'].mean()) / diversity_df['residual'].std()\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPARISON: BIN vs REGRESSION APPROACH\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Shows that are outliers in one but not the other\n",
    "bin_outliers = set(diversity_df[abs(diversity_df['z_within_bin']) > threshold_moderate]['SHOW'])\n",
    "reg_outliers = set(diversity_df[abs(diversity_df['z_regression']) > threshold_moderate]['SHOW'])\n",
    "\n",
    "only_bin = bin_outliers - reg_outliers\n",
    "only_reg = reg_outliers - bin_outliers\n",
    "both = bin_outliers & reg_outliers\n",
    "\n",
    "print(f\"\\nOutliers detected (|z| > 1.5):\")\n",
    "print(f\"  Bin approach: {len(bin_outliers)} shows\")\n",
    "print(f\"  Regression approach: {len(reg_outliers)} shows\")\n",
    "print(f\"  In both: {len(both)} shows\")\n",
    "print(f\"  Only in bin approach: {len(only_bin)} shows\")\n",
    "print(f\"  Only in regression: {len(only_reg)} shows\")\n",
    "\n",
    "if len(only_bin) > 0:\n",
    "    print(f\"\\nShows that are outliers with BIN approach but NOT regression:\")\n",
    "    for show in list(only_bin)[:10]:  # Show first 10\n",
    "        z_bin = diversity_df.loc[diversity_df['SHOW'] == show, 'z_within_bin'].values[0]\n",
    "        z_reg = diversity_df.loc[diversity_df['SHOW'] == show, 'z_regression'].values[0]\n",
    "        bin_label = diversity_df.loc[diversity_df['SHOW'] == show, 'size_bin'].values[0]\n",
    "        print(f\"  - {show} ({bin_label}): z_bin={z_bin:.2f}, z_reg={z_reg:.2f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12), constrained_layout=True)\n",
    "\n",
    "# Plot 1: Diversity by bin (box plot)\n",
    "ax1 = axes[0, 0]\n",
    "bin_order = bin_labels\n",
    "sns.boxplot(data=diversity_df, x='size_bin', y='shannon_entropy_top15', \n",
    "            order=bin_order, ax=ax1)\n",
    "\n",
    "# Add individual points\n",
    "sns.stripplot(data=diversity_df, x='size_bin', y='shannon_entropy_top15',\n",
    "              order=bin_order, ax=ax1, color='black', alpha=0.3, size=4)\n",
    "\n",
    "# Highlight outliers\n",
    "outlier_data = diversity_df[abs(diversity_df['z_within_bin']) > threshold_moderate]\n",
    "if len(outlier_data) > 0:\n",
    "    # Map bin labels to positions\n",
    "    bin_positions = {label: i for i, label in enumerate(bin_order)}\n",
    "    x_positions = [bin_positions[label] for label in outlier_data['size_bin']]\n",
    "    ax1.scatter(x=x_positions,\n",
    "               y=outlier_data['shannon_entropy_top15'],\n",
    "               s=100, facecolors='none', edgecolors='red', linewidths=2,\n",
    "               label='Outliers', zorder=5)\n",
    "\n",
    "ax1.set_xlabel('Sample Size Bin')\n",
    "ax1.set_ylabel('Shannon Entropy (Top 15 Emotions)')\n",
    "ax1.set_title('Diversity Distribution by Sample Size Bin\\nRed circles = outliers within bin')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Plot 2: Z-scores within bin\n",
    "ax2 = axes[0, 1]\n",
    "colors_map = {'Normal': 'gray', 'More diverse': 'lightblue', 'Much MORE diverse': 'darkblue',\n",
    "              'Less diverse': 'lightcoral', 'Much LESS diverse': 'darkred'}\n",
    "\n",
    "for category in diversity_df['outlier_category_bin'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category_bin'] == category]\n",
    "    ax2.scatter(subset['total_comments'], subset['z_within_bin'], \n",
    "               label=category, alpha=0.7, s=60, color=colors_map.get(category, 'gray'))\n",
    "\n",
    "ax2.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.5)\n",
    "ax2.axhline(threshold_moderate, color='blue', linestyle=':', alpha=0.5)\n",
    "ax2.axhline(-threshold_moderate, color='red', linestyle=':', alpha=0.5)\n",
    "\n",
    "# Add vertical lines for bin boundaries\n",
    "for edge in bin_edges[1:-1]:  # Skip first and last\n",
    "    ax2.axvline(edge, color='gray', linestyle='--', alpha=0.3, linewidth=1)\n",
    "\n",
    "ax2.set_xlabel('Total Comments (log scale)')\n",
    "ax2.set_ylabel('Z-score Within Bin')\n",
    "ax2.set_title('Z-Scores Within Sample Size Bins\\n(Outliers relative to bin peers)')\n",
    "ax2.set_xscale('log')\n",
    "ax2.legend(loc='best', fontsize=8)\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Add bin labels at top\n",
    "for i, (edge_start, edge_end, label) in enumerate(zip(bin_edges[:-1], bin_edges[1:], bin_labels)):\n",
    "    mid = np.sqrt(edge_start * edge_end) if edge_end != float('inf') else edge_start * 3\n",
    "    ax2.text(mid, ax2.get_ylim()[1] * 0.9, label, \n",
    "            ha='center', fontsize=9, style='italic', alpha=0.7)\n",
    "\n",
    "# Label outliers\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    if row['outlier_category_bin'] != 'Normal':\n",
    "        ax2.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=7, alpha=0.8)\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax2.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=8, alpha=0.9, fontweight='bold')\n",
    "\n",
    "# Plot 3: Percentile ranks within bin\n",
    "ax3 = axes[0, 2]\n",
    "for bin_label in bin_labels:\n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label]\n",
    "    ax3.hist(bin_data['percentile_within_bin'], bins=20, alpha=0.5, \n",
    "            label=bin_label, edgecolor='black')\n",
    "\n",
    "ax3.axvline(50, color='black', linestyle='--', linewidth=2, label='Median')\n",
    "ax3.set_xlabel('Percentile Rank Within Bin')\n",
    "ax3.set_ylabel('Number of Shows')\n",
    "ax3.set_title('Distribution of Within-Bin Percentile Ranks')\n",
    "ax3.legend()\n",
    "ax3.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Plot 4: Comparison with regression approach\n",
    "ax4 = axes[1, 0]\n",
    "scatter = ax4.scatter(diversity_df['z_regression'], diversity_df['z_within_bin'], \n",
    "           alpha=0.6, s=50, c=diversity_df['total_comments'], \n",
    "           cmap='viridis', norm=plt.matplotlib.colors.LogNorm())\n",
    "ax4.plot([-3, 3], [-3, 3], 'k--', alpha=0.3, label='y=x')\n",
    "ax4.axhline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax4.axvline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax4.set_xlabel('Z-score (Regression Approach)')\n",
    "ax4.set_ylabel('Z-score (Bin Approach)')\n",
    "ax4.set_title(f'Comparison: Bin vs Regression\\n(color = sample size, r={r_value:.3f})')\n",
    "ax4.legend()\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Fix colorbar creation\n",
    "try:\n",
    "    cbar = plt.colorbar(scatter, ax=ax4)\n",
    "    cbar.set_label('Total Comments')\n",
    "except Exception as e:\n",
    "    print(f\"Warning: Could not create colorbar for plot 4: {e}\")\n",
    "\n",
    "# Annotate shows that differ significantly between methods\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    z_diff = abs(row['z_regression'] - row['z_within_bin'])\n",
    "    if z_diff > 1.5:\n",
    "        ax4.annotate(row['SHOW'], \n",
    "                    (row['z_regression'], row['z_within_bin']),\n",
    "                    fontsize=7, alpha=0.7)\n",
    "\n",
    "# Plot 5: Mean diversity by bin with error bars\n",
    "ax5 = axes[1, 1]\n",
    "bin_stats = diversity_df.groupby('size_bin', observed=True)['shannon_entropy_top15'].agg(['mean', 'std', 'count'])\n",
    "bin_stats = bin_stats.reindex(bin_labels)\n",
    "\n",
    "x_pos = range(len(bin_labels))\n",
    "ax5.bar(x_pos, bin_stats['mean'], yerr=bin_stats['std'], \n",
    "       capsize=5, alpha=0.7, edgecolor='black', color='steelblue')\n",
    "ax5.set_xticks(x_pos)\n",
    "ax5.set_xticklabels(bin_labels, rotation=45, ha='right')\n",
    "ax5.set_ylabel('Mean Shannon Entropy (Top 15)')\n",
    "ax5.set_title('Mean Diversity by Sample Size Bin\\n(Error bars = ±1 SD)')\n",
    "ax5.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Add N labels\n",
    "for i, (idx, row) in enumerate(bin_stats.iterrows()):\n",
    "    ax5.text(i, row['mean'] + row['std'] + 0.05, f\"n={int(row['count'])}\", \n",
    "            ha='center', fontsize=9)\n",
    "\n",
    "# Plot 6: Outlier counts by bin (simplified to avoid rendering issues)\n",
    "ax6 = axes[1, 2]\n",
    "\n",
    "# Count outliers per bin per category\n",
    "outlier_summary = []\n",
    "for bin_label in bin_labels:\n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label]\n",
    "    for category in ['Much MORE diverse', 'More diverse', 'Normal', 'Less diverse', 'Much LESS diverse']:\n",
    "        count = (bin_data['outlier_category_bin'] == category).sum()\n",
    "        outlier_summary.append({\n",
    "            'bin': bin_label,\n",
    "            'category': category,\n",
    "            'count': count\n",
    "        })\n",
    "\n",
    "outlier_summary_df = pd.DataFrame(outlier_summary)\n",
    "\n",
    "# Create grouped bar chart instead of stacked\n",
    "categories_to_plot = ['Much MORE diverse', 'More diverse', 'Less diverse', 'Much LESS diverse']\n",
    "x_pos = np.arange(len(bin_labels))\n",
    "width = 0.2\n",
    "\n",
    "for i, category in enumerate(categories_to_plot):\n",
    "    counts = [outlier_summary_df[(outlier_summary_df['bin'] == bin_label) & \n",
    "                                  (outlier_summary_df['category'] == category)]['count'].values[0]\n",
    "              for bin_label in bin_labels]\n",
    "    ax6.bar(x_pos + i * width, counts, width, \n",
    "           label=category, color=colors_map.get(category, 'gray'), alpha=0.8)\n",
    "\n",
    "ax6.set_xlabel('Sample Size Bin')\n",
    "ax6.set_ylabel('Number of Shows')\n",
    "ax6.set_title('Outlier Counts by Bin')\n",
    "ax6.set_xticks(x_pos + width * 1.5)\n",
    "ax6.set_xticklabels(bin_labels, rotation=15, ha='right', fontsize=8)\n",
    "ax6.legend(fontsize=7, loc='upper left')\n",
    "ax6.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "plt.savefig('Figures/diversity_outliers_bin_approach.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'diversity_outliers_bin_approach.png'\")\n",
    "\n",
    "# Save standalone version of plot 2 (main outlier plot)\n",
    "print(\"Saving main outlier plot as separate image...\")\n",
    "fig_standalone = plt.figure(figsize=(10, 6))\n",
    "ax_standalone = fig_standalone.add_subplot(111)\n",
    "\n",
    "for category in diversity_df['outlier_category_bin'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category_bin'] == category]\n",
    "    ax_standalone.scatter(subset['total_comments'], subset['z_within_bin'], \n",
    "               label=category, alpha=0.7, s=80, color=colors_map.get(category, 'gray'))\n",
    "\n",
    "ax_standalone.axhline(0, color='black', linestyle='--', linewidth=1.5, alpha=0.5)\n",
    "ax_standalone.axhline(threshold_moderate, color='blue', linestyle=':', linewidth=1.5, alpha=0.5)\n",
    "ax_standalone.axhline(-threshold_moderate, color='red', linestyle=':', linewidth=1.5, alpha=0.5)\n",
    "\n",
    "# Add vertical lines for bin boundaries\n",
    "for edge in bin_edges[1:-1]:\n",
    "    ax_standalone.axvline(edge, color='gray', linestyle='--', alpha=0.3, linewidth=2)\n",
    "\n",
    "ax_standalone.set_xlabel('Total Comments (log scale)', fontsize=13)\n",
    "ax_standalone.set_ylabel('Z-score Within Bin', fontsize=13)\n",
    "ax_standalone.set_title('Emotional Diversity Outliers Within Sample Size Bins\\n(Top 15 Emotions)', fontsize=15)\n",
    "ax_standalone.set_xscale('log')\n",
    "ax_standalone.legend(loc='best', fontsize=11)\n",
    "ax_standalone.grid(True, alpha=0.3)\n",
    "\n",
    "# Add bin labels\n",
    "#for i, (edge_start, edge_end, label) in enumerate(zip(bin_edges[:-1], bin_edges[1:], bin_labels)):\n",
    "#    mid = np.sqrt(edge_start * edge_end) if edge_end != float('inf') else edge_start * 3\n",
    "#    ax_standalone.text(mid, ax_standalone.get_ylim()[1] * 0.92, label, \n",
    "#            ha='center', fontsize=11, style='italic', alpha=0.7,\n",
    "#            bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.3))\n",
    "\n",
    "# Label outliers and Squid Game\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    if row['outlier_category_bin'] != 'Normal':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=9, alpha=0.85)\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=10, alpha=0.95, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Figures/Figure16.png', dpi=300, bbox_inches='tight')\n",
    "print(\"Standalone bin outlier plot saved as 'diversity_outliers_bin_standalone.png'\")\n",
    "plt.close(fig_standalone)\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "output_file = 'Data/diversity_with_outliers_bin_approach.csv'\n",
    "diversity_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nResults saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed92f5b5-4c58-4a39-b3cc-435abad679db",
   "metadata": {},
   "source": [
    "### Run Simple Analytics to look at Squid Game's position in the System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de90ec61-e234-4294-b033-07a0f381e1b0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "SQUID GAME COMPARATIVE ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "Analyzing 50 shows across 15 emotions\n",
      "\n",
      "================================================================================\n",
      "1. Z-SCORE ANALYSIS: How different is Squid Game?\n",
      "================================================================================\n",
      "\n",
      "Squid Game's most distinctive emotions (by z-score):\n",
      "\n",
      "Highest (more than other shows):\n",
      "Admiration     0.195481\n",
      "Disapproval    0.231475\n",
      "Realization    0.949756\n",
      "Approval       2.108087\n",
      "Sadness        2.491873\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "Lowest (less than other shows):\n",
      "Joy         -0.762532\n",
      "Amusement   -0.602665\n",
      "Love        -0.599968\n",
      "Curiosity   -0.541867\n",
      "Desire      -0.539103\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "================================================================================\n",
      "2. MOST/LEAST SIMILAR SHOWS TO SQUID GAME\n",
      "================================================================================\n",
      "\n",
      "Top 10 most similar shows (cosine similarity - pattern-based):\n",
      "   1. Better Call Saul               (similarity: 0.9838)\n",
      "   2. Stranger Things                (similarity: 0.9674)\n",
      "   3. Lucifer                        (similarity: 0.9659)\n",
      "   4. Attack On Titan                (similarity: 0.9653)\n",
      "   5. The Walking Dead               (similarity: 0.9650)\n",
      "   6. The Umbrella Academy           (similarity: 0.9649)\n",
      "   7. Marvel's Agents Of S.H.I.E.L.D. (similarity: 0.9626)\n",
      "   8. The 100                        (similarity: 0.9624)\n",
      "   9. The Falcon And The Winter Soldier (similarity: 0.9601)\n",
      "  10. 13 Reasons Why                 (similarity: 0.9562)\n",
      "\n",
      "Top 10 least similar shows (cosine similarity - pattern-based):\n",
      "   1. Dark                           (similarity: 0.8633)\n",
      "   2. South Park                     (similarity: 0.8640)\n",
      "   3. Brooklyn Nine-Nine             (similarity: 0.8683)\n",
      "   4. PAW Patrol                     (similarity: 0.8782)\n",
      "   5. SpongeBob SquarePants          (similarity: 0.8876)\n",
      "   6. American Horror Story          (similarity: 0.8906)\n",
      "   7. Hawkeye                        (similarity: 0.8907)\n",
      "   8. The Big Bang Theory            (similarity: 0.9100)\n",
      "   9. Grey's Anatomy                 (similarity: 0.9206)\n",
      "  10. The Mandalorian                (similarity: 0.9233)\n",
      "\n",
      "Top 10 closest shows (Euclidean distance - magnitude-based):\n",
      "   1. Stranger Things                (distance: 2.8215)\n",
      "   2. Better Call Saul               (distance: 2.8933)\n",
      "   3. The Umbrella Academy           (distance: 2.8980)\n",
      "   4. The Walking Dead               (distance: 2.9267)\n",
      "   5. Lucifer                        (distance: 2.9586)\n",
      "   6. The 100                        (distance: 3.0195)\n",
      "   7. The Falcon And The Winter Soldier (distance: 3.1438)\n",
      "   8. Attack On Titan                (distance: 3.2327)\n",
      "   9. Sweet Home                     (distance: 3.2890)\n",
      "  10. Arrow                          (distance: 3.3305)\n",
      "\n",
      "================================================================================\n",
      "3. PERCENTILE RANKINGS: Where does Squid Game rank on each emotion?\n",
      "================================================================================\n",
      "\n",
      "Squid Game's percentile rankings:\n",
      "\n",
      "Highest percentiles (top of distribution):\n",
      "Sadness           98.0\n",
      "Approval          98.0\n",
      "Realization       90.0\n",
      "Admiration        64.0\n",
      "Disappointment    62.0\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "Lowest percentiles (bottom of distribution):\n",
      "Love         32.0\n",
      "Curiosity    28.0\n",
      "Desire       28.0\n",
      "Amusement    26.0\n",
      "Joy          26.0\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "================================================================================\n",
      "4. WHICH EMOTIONS MAKE SQUID GAME MOST DISTINCTIVE?\n",
      "================================================================================\n",
      "\n",
      "Contribution to overall distinctiveness (squared z-scores):\n",
      "Sadness           6.209430\n",
      "Approval          4.444033\n",
      "Realization       0.902036\n",
      "Joy               0.581455\n",
      "Amusement         0.363206\n",
      "Love              0.359961\n",
      "Curiosity         0.293620\n",
      "Desire            0.290632\n",
      "Optimism          0.202820\n",
      "Annoyance         0.111861\n",
      "Disapproval       0.053581\n",
      "Admiration        0.038213\n",
      "Confusion         0.036330\n",
      "Anger             0.011586\n",
      "Disappointment    0.000118\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "Total distinctiveness: 13.90\n",
      "Top 3 emotions account for 83.1% of distinctiveness\n",
      "\n",
      "================================================================================\n",
      "5. PCA ANALYSIS: Position in emotion space\n",
      "================================================================================\n",
      "\n",
      "PC1 explains 29.5% of variance\n",
      "PC2 explains 18.2% of variance\n",
      "Combined: 47.7% of variance\n",
      "\n",
      "Squid Game position: PC1=0.372, PC2=0.747\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'squid_game_comparative_analysis.png'\n",
      "\n",
      "Detailed comparison data saved to 'Sentiment_Data/squid_game_show_comparisons.csv'\n",
      "\n",
      "================================================================================\n",
      "ANALYSIS COMPLETE\n",
      "================================================================================\n",
      "\n",
      "================================================================================\n",
      "KEY INSIGHTS SUMMARY\n",
      "================================================================================\n",
      "\n",
      "1. MOST SIMILAR SHOWS:\n",
      "   - By pattern (cosine): Better Call Saul\n",
      "   - By magnitude (Euclidean): Stranger Things\n",
      "\n",
      "2. MOST DISTINCTIVE EMOTIONS:\n",
      "   - Primary: Sadness (contributes 44.7% to distinctiveness)\n",
      "\n",
      "3. EXTREME PERCENTILES:\n",
      "   - Highest: Sadness (98.0th percentile)\n",
      "   - Lowest: Joy (26.0th percentile)\n",
      "\n",
      "4. POSITION IN EMOTION SPACE:\n",
      "   - PC1: 0.372 (explains 29.5% of variance)\n",
      "   - PC2: 0.747 (explains 18.2% of variance)\n",
      "\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import zscore\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n",
    "from math import pi\n",
    "\n",
    "# Load data\n",
    "df = pd.read_csv('Data/Final_InSentiment_ByShow.csv')\n",
    "\n",
    "# Define top 15 emotions\n",
    "top15_emotions = [\n",
    "    \"[0, 'Admiration']\", \"[4, 'Approval']\", \"[18, 'Love']\", \n",
    "    \"[10, 'Disapproval']\", \"[3, 'Annoyance']\", \"[6, 'Confusion']\",\n",
    "    \"[1, 'Amusement']\", \"[25, 'Sadness']\", \"[9, 'Disappointment']\", \n",
    "    \"[22, 'Realization']\", \"[7, 'Curiosity']\", \"[2, 'Anger']\",\n",
    "    \"[17, 'Joy']\", \"[20, 'Optimism']\", \"[8, 'Desire']\"\n",
    "]\n",
    "\n",
    "# Filter to top 15 emotions (excluding Neutral for this analysis)\n",
    "filtered_df = df[df['PREDICTION'].isin(top15_emotions)]\n",
    "\n",
    "# Pivot to get shows x emotions matrix\n",
    "pivot_df = filtered_df.pivot_table(\n",
    "    index='SHOW', \n",
    "    columns='PREDICTION', \n",
    "    values='PERCENTAGES',\n",
    "    fill_value=0\n",
    ")\n",
    "\n",
    "# Clean up column names for readability\n",
    "pivot_df.columns = [col.split(\"'\")[1] for col in pivot_df.columns]\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"SQUID GAME COMPARATIVE ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "print(f\"\\nAnalyzing {len(pivot_df)} shows across {len(pivot_df.columns)} emotions\")\n",
    "\n",
    "# ============================================================================\n",
    "# 1. Z-SCORE ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"1. Z-SCORE ANALYSIS: How different is Squid Game?\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "z_df = pivot_df.apply(zscore)\n",
    "sg_z = z_df.loc['Squid Game'].sort_values()\n",
    "\n",
    "print(\"\\nSquid Game's most distinctive emotions (by z-score):\")\n",
    "print(\"\\nHighest (more than other shows):\")\n",
    "print(sg_z.tail(5))\n",
    "print(\"\\nLowest (less than other shows):\")\n",
    "print(sg_z.head(5))\n",
    "\n",
    "# ============================================================================\n",
    "# 2. NEAREST NEIGHBORS ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"2. MOST/LEAST SIMILAR SHOWS TO SQUID GAME\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Cosine similarity (pattern-based)\n",
    "similarities = cosine_similarity(pivot_df)\n",
    "sim_df = pd.DataFrame(similarities, index=pivot_df.index, columns=pivot_df.index)\n",
    "squid_game_similar_cosine = sim_df['Squid Game'].sort_values(ascending=False)[1:11]\n",
    "\n",
    "print(\"\\nTop 10 most similar shows (cosine similarity - pattern-based):\")\n",
    "for i, (show, sim) in enumerate(squid_game_similar_cosine.items(), 1):\n",
    "    print(f\"  {i:2d}. {show:<30s} (similarity: {sim:.4f})\")\n",
    "\n",
    "squid_game_similar_cosine = sim_df['Squid Game'].sort_values(ascending=True)[1:11]\n",
    "print(\"\\nTop 10 least similar shows (cosine similarity - pattern-based):\")\n",
    "for i, (show, sim) in enumerate(squid_game_similar_cosine.items(), 1):\n",
    "    print(f\"  {i:2d}. {show:<30s} (similarity: {sim:.4f})\")\n",
    "\n",
    "#revert back\n",
    "squid_game_similar_cosine = sim_df['Squid Game'].sort_values(ascending=False)[1:11]\n",
    "\n",
    "# Euclidean distance (magnitude-based)\n",
    "distances = euclidean_distances(pivot_df)\n",
    "dist_df = pd.DataFrame(distances, index=pivot_df.index, columns=pivot_df.index)\n",
    "squid_game_closest_euclidean = dist_df['Squid Game'].sort_values()[1:11]\n",
    "\n",
    "print(\"\\nTop 10 closest shows (Euclidean distance - magnitude-based):\")\n",
    "for i, (show, dist) in enumerate(squid_game_closest_euclidean.items(), 1):\n",
    "    print(f\"  {i:2d}. {show:<30s} (distance: {dist:.4f})\")\n",
    "\n",
    "# ============================================================================\n",
    "# 3. PERCENTILE RANKING\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"3. PERCENTILE RANKINGS: Where does Squid Game rank on each emotion?\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "percentile_ranks = pivot_df.rank(pct=True).loc['Squid Game'] * 100\n",
    "percentile_ranks_sorted = percentile_ranks.sort_values(ascending=False)\n",
    "\n",
    "print(\"\\nSquid Game's percentile rankings:\")\n",
    "print(\"\\nHighest percentiles (top of distribution):\")\n",
    "print(percentile_ranks_sorted.head(5))\n",
    "print(\"\\nLowest percentiles (bottom of distribution):\")\n",
    "print(percentile_ranks_sorted.tail(5))\n",
    "\n",
    "# ============================================================================\n",
    "# 4. DISTINCTIVENESS CONTRIBUTION\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"4. WHICH EMOTIONS MAKE SQUID GAME MOST DISTINCTIVE?\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Squared z-scores show contribution to overall distance\n",
    "sg_distinctiveness = (z_df.loc['Squid Game'] ** 2).sort_values(ascending=False)\n",
    "\n",
    "print(\"\\nContribution to overall distinctiveness (squared z-scores):\")\n",
    "print(sg_distinctiveness)\n",
    "\n",
    "total_distinctiveness = sg_distinctiveness.sum()\n",
    "print(f\"\\nTotal distinctiveness: {total_distinctiveness:.2f}\")\n",
    "print(f\"Top 3 emotions account for {sg_distinctiveness.head(3).sum()/total_distinctiveness*100:.1f}% of distinctiveness\")\n",
    "\n",
    "# ============================================================================\n",
    "# 5. PCA ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"5. PCA ANALYSIS: Position in emotion space\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(pivot_df)\n",
    "\n",
    "# PCA to 2 components\n",
    "pca = PCA(n_components=2)\n",
    "pca_coords = pca.fit_transform(scaled_data)\n",
    "\n",
    "print(f\"\\nPC1 explains {pca.explained_variance_ratio_[0]*100:.1f}% of variance\")\n",
    "print(f\"PC2 explains {pca.explained_variance_ratio_[1]*100:.1f}% of variance\")\n",
    "print(f\"Combined: {sum(pca.explained_variance_ratio_)*100:.1f}% of variance\")\n",
    "\n",
    "# Get Squid Game's position\n",
    "sg_idx = list(pivot_df.index).index('Squid Game')\n",
    "sg_pc1 = pca_coords[sg_idx, 0]\n",
    "sg_pc2 = pca_coords[sg_idx, 1]\n",
    "\n",
    "print(f\"\\nSquid Game position: PC1={sg_pc1:.3f}, PC2={sg_pc2:.3f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 12))\n",
    "\n",
    "# Plot 1: Z-scores\n",
    "ax1 = plt.subplot(2, 3, 1)\n",
    "colors = ['red' if x > 0 else 'blue' for x in sg_z.values]\n",
    "ax1.barh(range(len(sg_z)), sg_z.values, color=colors, alpha=0.7)\n",
    "ax1.set_yticks(range(len(sg_z)))\n",
    "ax1.set_yticklabels(sg_z.index)\n",
    "ax1.axvline(0, color='black', linestyle='--', linewidth=1)\n",
    "ax1.set_xlabel('Z-score')\n",
    "ax1.set_title('Squid Game Z-Scores vs All Shows\\n(Red=Above avg, Blue=Below avg)')\n",
    "ax1.grid(True, alpha=0.3, axis='x')\n",
    "\n",
    "# Plot 2: Percentile rankings\n",
    "ax2 = plt.subplot(2, 3, 2)\n",
    "colors = ['red' if x > 50 else 'blue' for x in percentile_ranks_sorted.values]\n",
    "ax2.barh(range(len(percentile_ranks_sorted)), percentile_ranks_sorted.values, color=colors, alpha=0.7)\n",
    "ax2.set_yticks(range(len(percentile_ranks_sorted)))\n",
    "ax2.set_yticklabels(percentile_ranks_sorted.index)\n",
    "ax2.axvline(50, color='black', linestyle='--', linewidth=1, label='Median')\n",
    "ax2.set_xlabel('Percentile Rank')\n",
    "ax2.set_title('Squid Game Percentile Rankings\\n(50 = median show)')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3, axis='x')\n",
    "\n",
    "# Plot 3: Distinctiveness contribution\n",
    "ax3 = plt.subplot(2, 3, 3)\n",
    "top_distinctive = sg_distinctiveness.head(8)\n",
    "ax3.pie(top_distinctive.values, labels=top_distinctive.index, autopct='%1.1f%%',\n",
    "        startangle=90)\n",
    "ax3.set_title('Top 8 Emotions Contributing to\\nSquid Game\\'s Distinctiveness')\n",
    "\n",
    "# Plot 4: PCA scatter\n",
    "ax4 = plt.subplot(2, 3, 4)\n",
    "ax4.scatter(pca_coords[:, 0], pca_coords[:, 1], alpha=0.5, s=50)\n",
    "\n",
    "# Highlight Squid Game\n",
    "ax4.scatter(pca_coords[sg_idx, 0], pca_coords[sg_idx, 1], \n",
    "           color='red', s=300, marker='*', label='Squid Game', zorder=5)\n",
    "\n",
    "# Annotate Squid Game\n",
    "ax4.annotate('Squid Game', \n",
    "            xy=(pca_coords[sg_idx, 0], pca_coords[sg_idx, 1]),\n",
    "            xytext=(10, 10), textcoords='offset points',\n",
    "            fontsize=10, fontweight='bold', color='red')\n",
    "\n",
    "# Annotate nearest neighbors\n",
    "threshold = np.percentile(dist_df.loc['Squid Game'], 10)  # Top 10% closest\n",
    "for i, show in enumerate(pivot_df.index):\n",
    "    if show != 'Squid Game' and dist_df.loc['Squid Game', show] < threshold:\n",
    "        ax4.annotate(show, \n",
    "                    (pca_coords[i, 0], pca_coords[i, 1]),\n",
    "                    fontsize=8, alpha=0.7)\n",
    "        # Draw line to Squid Game\n",
    "        ax4.plot([pca_coords[sg_idx, 0], pca_coords[i, 0]], \n",
    "                [pca_coords[sg_idx, 1], pca_coords[i, 1]], \n",
    "                'r--', alpha=0.3, linewidth=0.5)\n",
    "\n",
    "ax4.set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}% variance)')\n",
    "ax4.set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}% variance)')\n",
    "ax4.set_title('Shows in Emotion Space (PCA)\\nLines show closest neighbors')\n",
    "ax4.legend()\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 5: Radar plot comparison\n",
    "ax5 = plt.subplot(2, 3, 5, projection='polar')\n",
    "\n",
    "# Get profiles to compare\n",
    "mean_profile = pivot_df.mean()\n",
    "sg_profile = pivot_df.loc['Squid Game']\n",
    "most_similar_show = squid_game_similar_cosine.index[0]\n",
    "similar_profile = pivot_df.loc[most_similar_show]\n",
    "\n",
    "categories = list(pivot_df.columns)\n",
    "N = len(categories)\n",
    "angles = [n / float(N) * 2 * pi for n in range(N)]\n",
    "angles += angles[:1]\n",
    "\n",
    "# Plot each profile\n",
    "for name, profile, color in [\n",
    "    ('Mean', mean_profile, 'gray'),\n",
    "    ('Squid Game', sg_profile, 'red'),\n",
    "    (f'{most_similar_show}', similar_profile, 'blue')\n",
    "]:\n",
    "    values = profile.tolist()\n",
    "    values += values[:1]\n",
    "    ax5.plot(angles, values, label=name, linewidth=2, color=color)\n",
    "    ax5.fill(angles, values, alpha=0.1, color=color)\n",
    "\n",
    "ax5.set_xticks(angles[:-1])\n",
    "ax5.set_xticklabels(categories, size=8)\n",
    "ax5.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))\n",
    "ax5.set_title('Emotion Profile Comparison\\n(Squid Game vs Mean vs Most Similar)')\n",
    "\n",
    "# Plot 6: Distance distribution\n",
    "ax6 = plt.subplot(2, 3, 6)\n",
    "all_distances = dist_df.loc['Squid Game'].values\n",
    "sg_distance_percentile = (all_distances < 0).sum() / len(all_distances) * 100\n",
    "\n",
    "ax6.hist(all_distances, bins=30, edgecolor='black', alpha=0.7, color='steelblue')\n",
    "ax6.axvline(0, color='red', linestyle='--', linewidth=2, \n",
    "           label='Squid Game\\n(reference point)')\n",
    "ax6.set_xlabel('Euclidean Distance from Squid Game')\n",
    "ax6.set_ylabel('Number of Shows')\n",
    "ax6.set_title('Distribution of Shows by Distance from Squid Game')\n",
    "ax6.legend()\n",
    "ax6.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Figures/squid_game_comparative_analysis.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'squid_game_comparative_analysis.png'\")\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE DETAILED RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "# Create summary dataframe\n",
    "summary_df = pd.DataFrame({\n",
    "    'show': pivot_df.index,\n",
    "    'euclidean_distance': dist_df.loc['Squid Game'],\n",
    "    'cosine_similarity': sim_df.loc['Squid Game'],\n",
    "    'pc1': pca_coords[:, 0],\n",
    "    'pc2': pca_coords[:, 1]\n",
    "}).sort_values('euclidean_distance')\n",
    "\n",
    "output_file = 'Data/squid_game_show_comparisons.csv'\n",
    "summary_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nDetailed comparison data saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# ============================================================================\n",
    "# KEY INSIGHTS SUMMARY\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"KEY INSIGHTS SUMMARY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(f\"\\n1. MOST SIMILAR SHOWS:\")\n",
    "print(f\"   - By pattern (cosine): {squid_game_similar_cosine.index[0]}\")\n",
    "print(f\"   - By magnitude (Euclidean): {squid_game_closest_euclidean.index[0]}\")\n",
    "\n",
    "print(f\"\\n2. MOST DISTINCTIVE EMOTIONS:\")\n",
    "most_distinctive_emotion = sg_distinctiveness.index[0]\n",
    "print(f\"   - Primary: {most_distinctive_emotion} (contributes {sg_distinctiveness.iloc[0]/total_distinctiveness*100:.1f}% to distinctiveness)\")\n",
    "\n",
    "print(f\"\\n3. EXTREME PERCENTILES:\")\n",
    "highest_percentile = percentile_ranks_sorted.index[0]\n",
    "lowest_percentile = percentile_ranks_sorted.index[-1]\n",
    "print(f\"   - Highest: {highest_percentile} ({percentile_ranks_sorted.iloc[0]:.1f}th percentile)\")\n",
    "print(f\"   - Lowest: {lowest_percentile} ({percentile_ranks_sorted.iloc[-1]:.1f}th percentile)\")\n",
    "\n",
    "print(f\"\\n4. POSITION IN EMOTION SPACE:\")\n",
    "print(f\"   - PC1: {sg_pc1:.3f} (explains {pca.explained_variance_ratio_[0]*100:.1f}% of variance)\")\n",
    "print(f\"   - PC2: {sg_pc2:.3f} (explains {pca.explained_variance_ratio_[1]*100:.1f}% of variance)\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e379e4-6ded-4714-a1b5-e8372ba95110",
   "metadata": {},
   "source": [
    "### Prepare Data for Temporal Analysis\n",
    "\n",
    "*These next cells are purely for re-formatting of data and can be skipped*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fcfddc6-a7f1-415c-8c8f-f2148552684b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sentiment_df = pd.read_csv('Data/Final_InSentiment_Pred2.csv')\n",
    "sentiment_df.drop(['NER_COMMENT_BODY', 'COMMENT_BODY'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dc091339-8e77-40e2-914a-bedb15d9693e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Merging Sentiment Data with Timestamps\n",
      "============================================================\n",
      "\n",
      "1. Loading sentiment data from          Unnamed: 0 COMMENT_ID         SUBREDDIT              SHOW  \\\n",
      "0                 0    dwmhlce  r/bettercallsaul  Better Call Saul   \n",
      "1                 1    dwmixej  r/bettercallsaul  Better Call Saul   \n",
      "2                 2    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "3                 3    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "4                 4    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "...             ...        ...               ...               ...   \n",
      "2488048     2488048    grptghy         SweetHome        Sweet Home   \n",
      "2488049     2488049    grptghy         SweetHome        Sweet Home   \n",
      "2488050     2488050    grqh599         SweetHome        Sweet Home   \n",
      "2488051     2488051    gk1t9z5         SweetHome        Sweet Home   \n",
      "2488052     2488052    gk1t9z5         SweetHome        Sweet Home   \n",
      "\n",
      "                                           IN_CHAR_CONTEXT OUT_CHAR_CONTEXT  \\\n",
      "0        overthinking chuck has some sort of agoraphobi...               []   \n",
      "1        chuck got this allergi for electric before sau...               []   \n",
      "2        do we know this for sure  jimmy said at the ba...               []   \n",
      "3        but we dont know which came first the divorce ...               []   \n",
      "4        this for sure  jimmy said at the bar hearing t...               []   \n",
      "...                                                    ...              ...   \n",
      "2488048  to stay alive and see his beloved one which is...               []   \n",
      "2488049  his memory with jisu hes a special experiment ...               []   \n",
      "2488050  knew that if he had emotions he would look for...               []   \n",
      "2488051  willing to accept this interpetation i think y...               []   \n",
      "2488052  into mottion the catalyst the kids up until th...               []   \n",
      "\n",
      "        OUT_CHARS                                 IN_CHARS  IN_CHAR_COUNT  \\\n",
      "0              []                                ['chuck']              1   \n",
      "1              []                                ['chuck']              1   \n",
      "2              []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "3              []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "4              []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "...           ...                                      ...            ...   \n",
      "2488048        []                                 ['jisu']              1   \n",
      "2488049        []                                 ['jisu']              1   \n",
      "2488050        []                                 ['jisu']              1   \n",
      "2488051        []                               ['hyunsu']              1   \n",
      "2488052        []                               ['hyunsu']              1   \n",
      "\n",
      "         OUT_CHAR_COUNT                                          SENTIMENT  \\\n",
      "0                     0  {'Admiration': -6.52474308013916, 'Amusement':...   \n",
      "1                     0  {'Admiration': -5.565655708312988, 'Amusement'...   \n",
      "2                     0  {'Admiration': -5.733449935913086, 'Amusement'...   \n",
      "3                     0  {'Admiration': -6.154496669769287, 'Amusement'...   \n",
      "4                     0  {'Admiration': -5.68388557434082, 'Amusement':...   \n",
      "...                 ...                                                ...   \n",
      "2488048               0  {'Admiration': -5.667853355407715, 'Amusement'...   \n",
      "2488049               0  {'Admiration': -5.525354385375977, 'Amusement'...   \n",
      "2488050               0  {'Admiration': -6.203385829925537, 'Amusement'...   \n",
      "2488051               0  {'Admiration': -4.202151298522949, 'Amusement'...   \n",
      "2488052               0  {'Admiration': -6.262763023376465, 'Amusement'...   \n",
      "\n",
      "               PREDICTION      PREDICTION_2  \n",
      "0         [27, 'Neutral']   [27, 'Neutral']  \n",
      "1         [27, 'Neutral']   [27, 'Neutral']  \n",
      "2         [27, 'Neutral']   [27, 'Neutral']  \n",
      "3        [6, 'Confusion']  [6, 'Confusion']  \n",
      "4         [27, 'Neutral']   [27, 'Neutral']  \n",
      "...                   ...               ...  \n",
      "2488048   [27, 'Neutral']   [27, 'Neutral']  \n",
      "2488049   [24, 'Remorse']   [24, 'Remorse']  \n",
      "2488050   [27, 'Neutral']   [27, 'Neutral']  \n",
      "2488051   [4, 'Approval']   [4, 'Approval']  \n",
      "2488052   [27, 'Neutral']   [27, 'Neutral']  \n",
      "\n",
      "[2488053 rows x 13 columns]...\n",
      "   Loaded 2,488,053 rows\n",
      "   Columns: ['Unnamed: 0', 'COMMENT_ID', 'SUBREDDIT', 'SHOW', 'IN_CHAR_CONTEXT', 'OUT_CHAR_CONTEXT', 'OUT_CHARS', 'IN_CHARS', 'IN_CHAR_COUNT', 'OUT_CHAR_COUNT', 'SENTIMENT', 'PREDICTION', 'PREDICTION_2']\n",
      "   Unique shows: 50\n",
      "   Unique comment IDs: 1024893\n",
      "\n",
      "2. Loading subreddit-to-show mapping from Sentiment_Data/SubredditShowMapping.csv...\n",
      "   Loaded 69 subreddit mappings\n",
      "   Unique shows in mapping: 52\n",
      "\n",
      "3. Loading timestamp data from subreddit CSVs...\n",
      "   Found 68 subreddit CSV files\n",
      "   Processed 10 files, 465,568 comments...\n",
      "   Processed 20 files, 961,624 comments...\n",
      "   Processed 30 files, 1,613,738 comments...\n",
      "   Processed 40 files, 1,999,239 comments...\n",
      "   Processed 50 files, 2,551,446 comments...\n",
      "   Processed 60 files, 3,153,561 comments...\n",
      "\n",
      "   Successfully loaded 68 files with 3,625,117 total comments\n",
      "\n",
      "4. Combining timestamp data...\n",
      "   Total timestamp records: 3,625,117\n",
      "   Unique comment IDs with timestamps: 3,625,117\n",
      "\n",
      "5. Handling duplicate COMMENT_IDs in timestamp data...\n",
      "   Removed 0 duplicate timestamps\n",
      "   Unique timestamps remaining: 3,625,117\n",
      "\n",
      "6. Merging sentiment data with timestamps...\n",
      "   Before merge: 2,488,053 rows\n",
      "   After merge: 2,488,053 rows\n",
      "\n",
      "7. Results:\n",
      "   Rows with timestamps: 2,488,046 (100.0%)\n",
      "   Rows without timestamps: 7 (0.0%)\n",
      "\n",
      "8. Saving merged data to Sentiment_Data/Final_InSentiment_Time.csv...\n",
      "   Saved successfully!\n",
      "\n",
      "9. Sample of merged data:\n",
      "   Unnamed: 0 COMMENT_ID         SUBREDDIT              SHOW  \\\n",
      "0           0    dwmhlce  r/bettercallsaul  Better Call Saul   \n",
      "1           1    dwmixej  r/bettercallsaul  Better Call Saul   \n",
      "2           2    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "3           3    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "4           4    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "5           5    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "6           6    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "7           7    dwmriqu  r/bettercallsaul  Better Call Saul   \n",
      "8           8    dwmuovo  r/bettercallsaul  Better Call Saul   \n",
      "9           9    dwmuovo  r/bettercallsaul  Better Call Saul   \n",
      "\n",
      "                                     IN_CHAR_CONTEXT OUT_CHAR_CONTEXT  \\\n",
      "0  overthinking chuck has some sort of agoraphobi...               []   \n",
      "1  chuck got this allergi for electric before sau...               []   \n",
      "2  do we know this for sure  jimmy said at the ba...               []   \n",
      "3  but we dont know which came first the divorce ...               []   \n",
      "4  this for sure  jimmy said at the bar hearing t...               []   \n",
      "5  this for sure  jimmy said at the bar hearing t...               []   \n",
      "6  s illness first began when chuck and rebecca d...               []   \n",
      "7  bar hearing that chucks illness first began wh...               []   \n",
      "8  it was a flashback scene where chuck and jimmy...               []   \n",
      "9  it was a flashback scene where chuck and jimmy...               []   \n",
      "\n",
      "  OUT_CHARS                                 IN_CHARS  IN_CHAR_COUNT  \\\n",
      "0        []                                ['chuck']              1   \n",
      "1        []                                ['chuck']              1   \n",
      "2        []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "3        []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "4        []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "5        []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "6        []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "7        []  ['jimmy', 'chucks', 'chuck', 'rebecca']              4   \n",
      "8        []  ['chuck', 'jimmy', 'rebecca', 'chucks']              4   \n",
      "9        []  ['chuck', 'jimmy', 'rebecca', 'chucks']              4   \n",
      "\n",
      "   OUT_CHAR_COUNT                                          SENTIMENT  \\\n",
      "0               0  {'Admiration': -6.52474308013916, 'Amusement':...   \n",
      "1               0  {'Admiration': -5.565655708312988, 'Amusement'...   \n",
      "2               0  {'Admiration': -5.733449935913086, 'Amusement'...   \n",
      "3               0  {'Admiration': -6.154496669769287, 'Amusement'...   \n",
      "4               0  {'Admiration': -5.68388557434082, 'Amusement':...   \n",
      "5               0  {'Admiration': -4.991944789886475, 'Amusement'...   \n",
      "6               0  {'Admiration': -6.262171745300293, 'Amusement'...   \n",
      "7               0  {'Admiration': -6.553098678588867, 'Amusement'...   \n",
      "8               0  {'Admiration': -5.544386863708496, 'Amusement'...   \n",
      "9               0  {'Admiration': -5.609269142150879, 'Amusement'...   \n",
      "\n",
      "         PREDICTION      PREDICTION_2   TIME_DATETIME  \n",
      "0   [27, 'Neutral']   [27, 'Neutral']  4/1/2018 12:36  \n",
      "1   [27, 'Neutral']   [27, 'Neutral']  4/1/2018 13:14  \n",
      "2   [27, 'Neutral']   [27, 'Neutral']  4/1/2018 16:14  \n",
      "3  [6, 'Confusion']  [6, 'Confusion']  4/1/2018 16:14  \n",
      "4   [27, 'Neutral']   [27, 'Neutral']  4/1/2018 16:14  \n",
      "5   [27, 'Neutral']   [27, 'Neutral']  4/1/2018 16:14  \n",
      "6   [27, 'Neutral']   [25, 'Sadness']  4/1/2018 16:14  \n",
      "7   [27, 'Neutral']  [6, 'Confusion']  4/1/2018 16:14  \n",
      "8   [27, 'Neutral']   [27, 'Neutral']  4/1/2018 17:11  \n",
      "9   [27, 'Neutral']   [27, 'Neutral']  4/1/2018 17:11  \n",
      "\n",
      "============================================================\n",
      "Merge complete!\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "# Configuration\n",
    "SENTIMENT_FILE = sentiment_df  # load in previous cell\n",
    "MAPPING_FILE = \"Data/SubredditShowMapping.csv\"  # Update path if needed\n",
    "SUBREDDIT_DATA_DIR = r\"C:/Users/~/Desktop/UserData//\"  # Update to the directory containing subreddit CSVs\n",
    "OUTPUT_FILE = \"Data/Final_InSentiment_Time.csv\"\n",
    "\n",
    "print(\"=\" * 60)\n",
    "print(\"Merging Sentiment Data with Timestamps\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Load sentiment data\n",
    "print(f\"\\n1. Loading sentiment data from {SENTIMENT_FILE}...\")\n",
    "sentiment_df = SENTIMENT_FILE\n",
    "print(f\"   Loaded {len(sentiment_df):,} rows\")\n",
    "print(f\"   Columns: {list(sentiment_df.columns)}\")\n",
    "print(f\"   Unique shows: {sentiment_df['SHOW'].nunique()}\")\n",
    "print(f\"   Unique comment IDs: {sentiment_df['COMMENT_ID'].nunique()}\")\n",
    "\n",
    "# Load mapping\n",
    "print(f\"\\n2. Loading subreddit-to-show mapping from {MAPPING_FILE}...\")\n",
    "mapping_df = pd.read_csv(MAPPING_FILE)\n",
    "print(f\"   Loaded {len(mapping_df)} subreddit mappings\")\n",
    "\n",
    "# Create a dictionary for subreddit -> show mapping\n",
    "subreddit_to_show = dict(zip(mapping_df['SUBREDDIT'], mapping_df['SHOW']))\n",
    "print(f\"   Unique shows in mapping: {mapping_df['SHOW'].nunique()}\")\n",
    "\n",
    "# Initialize empty dataframe to collect all timestamp data\n",
    "print(f\"\\n3. Loading timestamp data from subreddit CSVs...\")\n",
    "all_timestamps = []\n",
    "\n",
    "# Find all subreddit CSV files\n",
    "subreddit_files = list(Path(SUBREDDIT_DATA_DIR).glob(\"*_UserData.csv\"))\n",
    "print(f\"   Found {len(subreddit_files)} subreddit CSV files\")\n",
    "\n",
    "if len(subreddit_files) == 0:\n",
    "    print(f\"\\n   WARNING: No files matching '*_UserData.csv' found in {SUBREDDIT_DATA_DIR}\")\n",
    "    print(f\"   Please update SUBREDDIT_DATA_DIR to the correct directory\")\n",
    "    exit(1)\n",
    "\n",
    "# Load each subreddit file\n",
    "files_loaded = 0\n",
    "comments_loaded = 0\n",
    "\n",
    "for file_path in subreddit_files:\n",
    "    subreddit_name = file_path.stem.replace(\"_UserData\", \"\")\n",
    "    \n",
    "    try:\n",
    "        # Load the subreddit data\n",
    "        sub_df = pd.read_csv(file_path)\n",
    "        \n",
    "        # Check if required columns exist\n",
    "        if 'COMMENT_ID' not in sub_df.columns:\n",
    "            print(f\"   WARNING: {file_path.name} missing COMMENT_ID column, skipping\")\n",
    "            continue\n",
    "            \n",
    "        if 'TIME_DATETIME' not in sub_df.columns:\n",
    "            print(f\"   WARNING: {file_path.name} missing TIME_DATETIME column, skipping\")\n",
    "            continue\n",
    "        \n",
    "        # Get the show name from mapping (if exists) or from file (if SHOW column exists)\n",
    "        if 'SHOW' in sub_df.columns:\n",
    "            show_name = sub_df['SHOW'].iloc[0] if len(sub_df) > 0 else None\n",
    "        elif subreddit_name in subreddit_to_show:\n",
    "            show_name = subreddit_to_show[subreddit_name]\n",
    "        else:\n",
    "            print(f\"   WARNING: {file_path.name} ({subreddit_name}) not in mapping, skipping\")\n",
    "            continue\n",
    "        \n",
    "        # Keep only COMMENT_ID and TIME_DATETIME\n",
    "        sub_timestamps = sub_df[['COMMENT_ID', 'TIME_DATETIME']].copy()\n",
    "        \n",
    "        all_timestamps.append(sub_timestamps)\n",
    "        files_loaded += 1\n",
    "        comments_loaded += len(sub_timestamps)\n",
    "        \n",
    "        if files_loaded % 10 == 0:\n",
    "            print(f\"   Processed {files_loaded} files, {comments_loaded:,} comments...\")\n",
    "            \n",
    "    except Exception as e:\n",
    "        print(f\"   ERROR loading {file_path.name}: {str(e)}\")\n",
    "        continue\n",
    "\n",
    "print(f\"\\n   Successfully loaded {files_loaded} files with {comments_loaded:,} total comments\")\n",
    "\n",
    "# Combine all timestamp data\n",
    "print(\"\\n4. Combining timestamp data...\")\n",
    "timestamps_df = pd.concat(all_timestamps, ignore_index=True)\n",
    "print(f\"   Total timestamp records: {len(timestamps_df):,}\")\n",
    "print(f\"   Unique comment IDs with timestamps: {timestamps_df['COMMENT_ID'].nunique():,}\")\n",
    "\n",
    "# Remove duplicates (keep first occurrence)\n",
    "print(\"\\n5. Handling duplicate COMMENT_IDs in timestamp data...\")\n",
    "before_dedup = len(timestamps_df)\n",
    "timestamps_df = timestamps_df.drop_duplicates(subset=['COMMENT_ID'], keep='first')\n",
    "after_dedup = len(timestamps_df)\n",
    "print(f\"   Removed {before_dedup - after_dedup:,} duplicate timestamps\")\n",
    "print(f\"   Unique timestamps remaining: {len(timestamps_df):,}\")\n",
    "\n",
    "# Merge with sentiment data\n",
    "print(\"\\n6. Merging sentiment data with timestamps...\")\n",
    "print(f\"   Before merge: {len(sentiment_df):,} rows\")\n",
    "\n",
    "merged_df = sentiment_df.merge(\n",
    "    timestamps_df, \n",
    "    on='COMMENT_ID', \n",
    "    how='left'\n",
    ")\n",
    "\n",
    "print(f\"   After merge: {len(merged_df):,} rows\")\n",
    "\n",
    "# Check for missing timestamps\n",
    "missing_timestamps = merged_df['TIME_DATETIME'].isna().sum()\n",
    "print(f\"\\n7. Results:\")\n",
    "print(f\"   Rows with timestamps: {len(merged_df) - missing_timestamps:,} ({100 * (len(merged_df) - missing_timestamps) / len(merged_df):.1f}%)\")\n",
    "print(f\"   Rows without timestamps: {missing_timestamps:,} ({100 * missing_timestamps / len(merged_df):.1f}%)\")\n",
    "\n",
    "# Save merged data\n",
    "print(f\"\\n8. Saving merged data to {OUTPUT_FILE}...\")\n",
    "merged_df.to_csv(OUTPUT_FILE, index=False)\n",
    "print(f\"   Saved successfully!\")\n",
    "\n",
    "# Show sample of data\n",
    "print(\"\\n9. Sample of merged data:\")\n",
    "print(merged_df.head(10))\n",
    "\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"Merge complete!\")\n",
    "print(\"=\" * 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e604d0-c9c9-42c3-ba53-edc54ce41b85",
   "metadata": {},
   "source": [
    "### Temporal Analysis\n",
    "\n",
    "*Source data for this step is not provided*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "92880a61-fd83-4df8-b3e3-ade3d05cbf6c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Temporal Distribution Analysis of Reddit Comments\n",
      "================================================================================\n",
      "\n",
      "1. Loading data from Sentiment_Data/Final_InSentiment_Time.csv...\n",
      "   Loaded 2,488,053 rows\n",
      "   Rows with timestamps: 2,488,046 (100.0%)\n",
      "\n",
      "2. Converting timestamps to datetime...\n",
      "   Warning: 2487997 timestamps failed to parse as Unix epoch, trying alternative formats...\n",
      "   Successfully parsed 2,488,046 timestamps\n",
      "   Date range: 2018-01-01 00:04:00 to 2022-12-31 23:58:00\n",
      "   Time span: 1825 days\n",
      "\n",
      "3. Computing show-level statistics...\n",
      "\n",
      "   Shows with temporal data: 50\n",
      "\n",
      "   Top 10 shows by total comments:\n",
      "               SHOW  TOTAL_COMMENTS  TIME_SPAN_DAYS  AVG_COMMENTS_PER_DAY\n",
      "    Game Of Thrones          248134            1823            136.113001\n",
      "          One Piece          204292            1815            112.557576\n",
      "    Attack On Titan          199773            1823            109.584750\n",
      "The Handmaid's Tale          152182            1811             84.032027\n",
      "        The Witcher          120505            1825             66.030137\n",
      "           Euphoria          116428            1286             90.534992\n",
      "   Better Call Saul          113205            1735             65.247839\n",
      "    Stranger Things           82115            1825             44.994521\n",
      "          Cobra Kai           81664            1702             47.981199\n",
      "  Dragon Ball Super           73096            1825             40.052603\n",
      "\n",
      "   Saved show statistics to Sentiment_Data/Temporal_Analysis/show_temporal_statistics.csv\n",
      "\n",
      "4. Analyzing temporal patterns for all shows...\n",
      "   Using window size: 28 days\n",
      "   Minimum comment threshold: 1000\n",
      "\n",
      "   Analyzed 50 shows\n",
      "   Shows meeting 1000+ comment threshold: 47\n",
      "   Shows below threshold: 3\n",
      "\n",
      "   Saved peak analysis to Sentiment_Data/Temporal_Analysis/peak_period_analysis.csv\n",
      "\n",
      "   Top 10 shows by peak intensity:\n",
      "               SHOW  peak_comments  peak_intensity_ratio  pct_of_total  meets_threshold\n",
      "    Game Of Thrones         119405             31.347509     48.121176             True\n",
      "           Euphoria          67156             26.512330     57.680283             True\n",
      "    Attack On Titan          40628             13.248157     20.337083             True\n",
      "           The Boys          32396             21.451269     47.897569             True\n",
      "The Handmaid's Tale          30186             12.836376     19.835460             True\n",
      "   Better Call Saul          29918             16.385460     26.428161             True\n",
      "    Stranger Things          25555             20.295331     31.120989             True\n",
      "               Dark          19085             26.005379     40.074332             True\n",
      "  The Wheel Of Time          17321             17.016116     56.251624             True\n",
      "        The Witcher          17320              9.373150     14.372848             True\n",
      "\n",
      "5. Creating visualizations for selected shows...\n",
      "   Creating plots for 8 shows...\n",
      "   Saved visualization to Sentiment_Data/Temporal_Analysis/temporal_patterns_sample_shows.png\n",
      "\n",
      "6. Creating summary statistics...\n",
      "\n",
      "   Summary Statistics:\n",
      "   Total shows analyzed: 50\n",
      "   Shows meeting 1000+ threshold: 47\n",
      "   Median peak intensity ratio: 9.81\n",
      "   Mean peak intensity ratio: 12.51\n",
      "   Median % of comments in peak: 19.88\n",
      "   Mean % of comments in peak: 26.34\n",
      "   Saved summary plots to Sentiment_Data/Temporal_Analysis/peak_distribution_summary.png\n",
      "\n",
      "================================================================================\n",
      "Analysis complete!\n",
      "Check the 'Sentiment_Data/Temporal_Analysis' directory for:\n",
      "  - show_temporal_statistics.csv\n",
      "  - peak_period_analysis.csv\n",
      "  - temporal_patterns_sample_shows.png\n",
      "  - peak_distribution_summary.png\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import timedelta\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Configuration\n",
    "INPUT_FILE = \"Data/Final_InSentiment_Time.csv\"  # Update path if needed\n",
    "OUTPUT_DIR = \"Data/Temporal_Analysis\"  # Directory for outputs\n",
    "\n",
    "print(\"=\" * 80)\n",
    "print(\"Temporal Distribution Analysis of Reddit Comments\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# Create output directory\n",
    "import os\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "\n",
    "# Load data\n",
    "print(f\"\\n1. Loading data from {INPUT_FILE}...\")\n",
    "df = pd.read_csv(INPUT_FILE)\n",
    "print(f\"   Loaded {len(df):,} rows\")\n",
    "\n",
    "# Filter to rows with timestamps\n",
    "df_with_time = df[df['TIME_DATETIME'].notna()].copy()\n",
    "print(f\"   Rows with timestamps: {len(df_with_time):,} ({100*len(df_with_time)/len(df):.1f}%)\")\n",
    "\n",
    "# Convert to datetime\n",
    "print(\"\\n2. Converting timestamps to datetime...\")\n",
    "# Handle both Unix timestamps (integers) and formatted strings\n",
    "df_with_time['DATETIME'] = pd.to_datetime(df_with_time['TIME_DATETIME'], unit='s', errors='coerce')\n",
    "\n",
    "# Check if there are any NaT (failed conversions) and try alternative format\n",
    "nat_count = df_with_time['DATETIME'].isna().sum()\n",
    "if nat_count > 0:\n",
    "    print(f\"   Warning: {nat_count} timestamps failed to parse as Unix epoch, trying alternative formats...\")\n",
    "    # For rows that failed, try parsing as string format\n",
    "    mask = df_with_time['DATETIME'].isna()\n",
    "    df_with_time.loc[mask, 'DATETIME'] = pd.to_datetime(df_with_time.loc[mask, 'TIME_DATETIME'], \n",
    "                                                          format='mixed', errors='coerce')\n",
    "    \n",
    "# Remove any rows that still couldn't be parsed\n",
    "df_with_time = df_with_time[df_with_time['DATETIME'].notna()].copy()\n",
    "df_with_time = df_with_time.sort_values('DATETIME')\n",
    "print(f\"   Successfully parsed {len(df_with_time):,} timestamps\")\n",
    "\n",
    "# Get date range\n",
    "print(f\"   Date range: {df_with_time['DATETIME'].min()} to {df_with_time['DATETIME'].max()}\")\n",
    "print(f\"   Time span: {(df_with_time['DATETIME'].max() - df_with_time['DATETIME'].min()).days} days\")\n",
    "\n",
    "# Show-level statistics\n",
    "print(\"\\n3. Computing show-level statistics...\")\n",
    "show_stats = df_with_time.groupby('SHOW').agg({\n",
    "    'COMMENT_ID': 'count',\n",
    "    'DATETIME': ['min', 'max']\n",
    "}).reset_index()\n",
    "\n",
    "show_stats.columns = ['SHOW', 'TOTAL_COMMENTS', 'FIRST_COMMENT', 'LAST_COMMENT']\n",
    "show_stats['TIME_SPAN_DAYS'] = (show_stats['LAST_COMMENT'] - show_stats['FIRST_COMMENT']).dt.days\n",
    "show_stats['AVG_COMMENTS_PER_DAY'] = show_stats['TOTAL_COMMENTS'] / show_stats['TIME_SPAN_DAYS']\n",
    "show_stats = show_stats.sort_values('TOTAL_COMMENTS', ascending=False)\n",
    "\n",
    "print(f\"\\n   Shows with temporal data: {len(show_stats)}\")\n",
    "print(\"\\n   Top 10 shows by total comments:\")\n",
    "print(show_stats[['SHOW', 'TOTAL_COMMENTS', 'TIME_SPAN_DAYS', 'AVG_COMMENTS_PER_DAY']].head(10).to_string(index=False))\n",
    "\n",
    "# Save show statistics\n",
    "show_stats.to_csv(f\"{OUTPUT_DIR}/show_temporal_statistics.csv\", index=False)\n",
    "print(f\"\\n   Saved show statistics to {OUTPUT_DIR}/show_temporal_statistics.csv\")\n",
    "\n",
    "\n",
    "# Function to calculate rolling intensity\n",
    "def calculate_rolling_intensity(show_df, window_days=28):\n",
    "    \"\"\"\n",
    "    Calculate rolling intensity for a show's comments\n",
    "    Returns a dataframe with date, comment count, and intensity metrics\n",
    "    \"\"\"\n",
    "    # Create daily counts\n",
    "    show_df = show_df.copy()\n",
    "    show_df['DATE'] = show_df['DATETIME'].dt.date\n",
    "    daily_counts = show_df.groupby('DATE').size().reset_index(name='COMMENTS')\n",
    "    daily_counts['DATE'] = pd.to_datetime(daily_counts['DATE'])\n",
    "    \n",
    "    # Create complete date range\n",
    "    date_range = pd.date_range(\n",
    "        start=daily_counts['DATE'].min(),\n",
    "        end=daily_counts['DATE'].max(),\n",
    "        freq='D'\n",
    "    )\n",
    "    \n",
    "    # Reindex to include all dates (fill with 0)\n",
    "    daily_counts = daily_counts.set_index('DATE').reindex(date_range, fill_value=0).reset_index()\n",
    "    daily_counts.columns = ['DATE', 'COMMENTS']\n",
    "    \n",
    "    # Calculate rolling metrics\n",
    "    daily_counts['ROLLING_SUM'] = daily_counts['COMMENTS'].rolling(window=window_days, min_periods=1).sum()\n",
    "    daily_counts['ROLLING_AVG'] = daily_counts['COMMENTS'].rolling(window=window_days, min_periods=1).mean()\n",
    "    \n",
    "    # Calculate intensity ratio (compared to overall average)\n",
    "    overall_avg = daily_counts['COMMENTS'].mean()\n",
    "    daily_counts['INTENSITY_RATIO'] = daily_counts['ROLLING_AVG'] / overall_avg if overall_avg > 0 else 0\n",
    "    \n",
    "    return daily_counts\n",
    "\n",
    "\n",
    "# Function to find peak period\n",
    "def find_peak_period(show_df, window_days=28, min_comments=1000):\n",
    "    \"\"\"\n",
    "    Find the peak intensity period for a show\n",
    "    Returns dict with peak info\n",
    "    \"\"\"\n",
    "    daily_counts = calculate_rolling_intensity(show_df, window_days)\n",
    "    \n",
    "    # Find the date with maximum rolling sum\n",
    "    peak_idx = daily_counts['ROLLING_SUM'].idxmax()\n",
    "    peak_date = daily_counts.loc[peak_idx, 'DATE']\n",
    "    peak_comments = daily_counts.loc[peak_idx, 'ROLLING_SUM']\n",
    "    peak_intensity = daily_counts.loc[peak_idx, 'INTENSITY_RATIO']\n",
    "    \n",
    "    # Define peak window\n",
    "    peak_start = peak_date - timedelta(days=window_days-1)\n",
    "    peak_end = peak_date\n",
    "    \n",
    "    # Get comments in peak window\n",
    "    peak_df = show_df[(show_df['DATETIME'].dt.date >= peak_start.date()) & \n",
    "                      (show_df['DATETIME'].dt.date <= peak_end.date())]\n",
    "    \n",
    "    return {\n",
    "        'peak_date': peak_date,\n",
    "        'peak_start': peak_start,\n",
    "        'peak_end': peak_end,\n",
    "        'peak_comments': int(peak_comments),\n",
    "        'actual_comments_in_window': len(peak_df),\n",
    "        'peak_intensity_ratio': peak_intensity,\n",
    "        'meets_threshold': len(peak_df) >= min_comments,\n",
    "        'pct_of_total': 100 * len(peak_df) / len(show_df)\n",
    "    }\n",
    "\n",
    "\n",
    "print(\"\\n4. Analyzing temporal patterns for all shows...\")\n",
    "print(f\"   Using window size: 28 days\")\n",
    "print(f\"   Minimum comment threshold: 1000\")\n",
    "\n",
    "# Analyze each show\n",
    "peak_results = []\n",
    "\n",
    "for show in show_stats['SHOW'].values:\n",
    "    show_df = df_with_time[df_with_time['SHOW'] == show].copy()\n",
    "    \n",
    "    if len(show_df) < 100:  # Skip shows with too few comments\n",
    "        continue\n",
    "    \n",
    "    peak_info = find_peak_period(show_df, window_days=28, min_comments=1000)\n",
    "    peak_info['SHOW'] = show\n",
    "    peak_info['TOTAL_COMMENTS'] = len(show_df)\n",
    "    peak_results.append(peak_info)\n",
    "\n",
    "peak_df = pd.DataFrame(peak_results)\n",
    "peak_df = peak_df.sort_values('peak_comments', ascending=False)\n",
    "\n",
    "print(f\"\\n   Analyzed {len(peak_df)} shows\")\n",
    "print(f\"   Shows meeting 1000+ comment threshold: {peak_df['meets_threshold'].sum()}\")\n",
    "print(f\"   Shows below threshold: {(~peak_df['meets_threshold']).sum()}\")\n",
    "\n",
    "# Save peak analysis\n",
    "peak_df.to_csv(f\"{OUTPUT_DIR}/peak_period_analysis.csv\", index=False)\n",
    "print(f\"\\n   Saved peak analysis to {OUTPUT_DIR}/peak_period_analysis.csv\")\n",
    "\n",
    "print(\"\\n   Top 10 shows by peak intensity:\")\n",
    "print(peak_df[['SHOW', 'peak_comments', 'peak_intensity_ratio', 'pct_of_total', 'meets_threshold']].head(10).to_string(index=False))\n",
    "\n",
    "\n",
    "print(\"\\n5. Creating visualizations for selected shows...\")\n",
    "\n",
    "# Select diverse shows for visualization\n",
    "shows_to_plot = []\n",
    "\n",
    "# Add top 3 by volume\n",
    "top_volume = peak_df.nlargest(3, 'TOTAL_COMMENTS')['SHOW'].values\n",
    "shows_to_plot.extend(top_volume)\n",
    "\n",
    "# Add top 3 by peak intensity\n",
    "top_intensity = peak_df.nlargest(3, 'peak_intensity_ratio')['SHOW'].values\n",
    "shows_to_plot.extend(top_intensity)\n",
    "\n",
    "# Add 3 with different patterns (if available)\n",
    "if len(peak_df) >= 9:\n",
    "    medium_shows = peak_df.iloc[len(peak_df)//3:len(peak_df)//3+3]['SHOW'].values\n",
    "    shows_to_plot.extend(medium_shows)\n",
    "\n",
    "# Remove duplicates\n",
    "shows_to_plot = list(dict.fromkeys(shows_to_plot))[:9]  # Max 9 for 3x3 grid\n",
    "\n",
    "print(f\"   Creating plots for {len(shows_to_plot)} shows...\")\n",
    "\n",
    "# Create figure with subplots\n",
    "n_shows = len(shows_to_plot)\n",
    "n_cols = 3\n",
    "n_rows = (n_shows + n_cols - 1) // n_cols\n",
    "\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(18, 5*n_rows))\n",
    "if n_rows == 1:\n",
    "    axes = axes.reshape(1, -1)\n",
    "axes = axes.flatten()\n",
    "\n",
    "for idx, show in enumerate(shows_to_plot):\n",
    "    show_df = df_with_time[df_with_time['SHOW'] == show].copy()\n",
    "    daily_counts = calculate_rolling_intensity(show_df, window_days=28)\n",
    "    peak_info = peak_df[peak_df['SHOW'] == show].iloc[0]\n",
    "    \n",
    "    ax = axes[idx]\n",
    "    \n",
    "    # Plot daily comments (thin line)\n",
    "    ax.plot(daily_counts['DATE'], daily_counts['COMMENTS'], \n",
    "            alpha=0.3, linewidth=0.5, color='gray', label='Daily comments')\n",
    "    \n",
    "    # Plot rolling average (thicker line)\n",
    "    ax.plot(daily_counts['DATE'], daily_counts['ROLLING_AVG'], \n",
    "            linewidth=2, color='steelblue', label='28-day rolling avg')\n",
    "    \n",
    "    # Highlight peak period\n",
    "    peak_start = peak_info['peak_start']\n",
    "    peak_end = peak_info['peak_end']\n",
    "    ax.axvspan(peak_start, peak_end, alpha=0.2, color='red', label='Peak period')\n",
    "    \n",
    "    # Format\n",
    "    ax.set_title(f\"{show}\\n({peak_info['TOTAL_COMMENTS']:,} total, {peak_info['peak_comments']:,} in peak, \"\n",
    "                 f\"{peak_info['peak_intensity_ratio']:.1f}x intensity)\", \n",
    "                 fontsize=10, fontweight='bold')\n",
    "    ax.set_xlabel('Date')\n",
    "    ax.set_ylabel('Comments per day')\n",
    "    ax.grid(alpha=0.3)\n",
    "    ax.legend(fontsize=8, loc='upper right')\n",
    "    \n",
    "    # Rotate x-axis labels\n",
    "    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "\n",
    "# Hide extra subplots\n",
    "for idx in range(len(shows_to_plot), len(axes)):\n",
    "    axes[idx].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{OUTPUT_DIR}/temporal_patterns_sample_shows.png\", dpi=150, bbox_inches='tight')\n",
    "print(f\"   Saved visualization to {OUTPUT_DIR}/temporal_patterns_sample_shows.png\")\n",
    "plt.close()\n",
    "\n",
    "\n",
    "print(\"\\n6. Creating summary statistics...\")\n",
    "\n",
    "# Distribution of peak characteristics\n",
    "summary_stats = {\n",
    "    'Total shows analyzed': len(peak_df),\n",
    "    'Shows meeting 1000+ threshold': peak_df['meets_threshold'].sum(),\n",
    "    'Median peak intensity ratio': peak_df['peak_intensity_ratio'].median(),\n",
    "    'Mean peak intensity ratio': peak_df['peak_intensity_ratio'].mean(),\n",
    "    'Median % of comments in peak': peak_df['pct_of_total'].median(),\n",
    "    'Mean % of comments in peak': peak_df['pct_of_total'].mean(),\n",
    "}\n",
    "\n",
    "print(\"\\n   Summary Statistics:\")\n",
    "for key, value in summary_stats.items():\n",
    "    print(f\"   {key}: {value:.2f}\" if isinstance(value, float) else f\"   {key}: {value}\")\n",
    "\n",
    "# Create distribution plots\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "# Plot 1: Peak intensity distribution\n",
    "axes[0, 0].hist(peak_df['peak_intensity_ratio'], bins=30, edgecolor='black', alpha=0.7)\n",
    "axes[0, 0].axvline(peak_df['peak_intensity_ratio'].median(), color='red', \n",
    "                   linestyle='--', linewidth=2, label=f'Median: {peak_df[\"peak_intensity_ratio\"].median():.1f}')\n",
    "axes[0, 0].set_xlabel('Peak Intensity Ratio')\n",
    "axes[0, 0].set_ylabel('Number of Shows')\n",
    "axes[0, 0].set_title('Distribution of Peak Intensity Ratios')\n",
    "axes[0, 0].legend()\n",
    "axes[0, 0].grid(alpha=0.3)\n",
    "\n",
    "# Plot 2: Percentage of comments in peak\n",
    "axes[0, 1].hist(peak_df['pct_of_total'], bins=30, edgecolor='black', alpha=0.7, color='green')\n",
    "axes[0, 1].axvline(peak_df['pct_of_total'].median(), color='red', \n",
    "                   linestyle='--', linewidth=2, label=f'Median: {peak_df[\"pct_of_total\"].median():.1f}%')\n",
    "axes[0, 1].set_xlabel('% of Total Comments in Peak Period')\n",
    "axes[0, 1].set_ylabel('Number of Shows')\n",
    "axes[0, 1].set_title('Distribution of Comment Concentration in Peak')\n",
    "axes[0, 1].legend()\n",
    "axes[0, 1].grid(alpha=0.3)\n",
    "\n",
    "# Plot 3: Peak comments vs total comments\n",
    "axes[1, 0].scatter(peak_df['TOTAL_COMMENTS'], peak_df['peak_comments'], alpha=0.6)\n",
    "axes[1, 0].axhline(1000, color='red', linestyle='--', linewidth=2, label='1000 comment threshold')\n",
    "axes[1, 0].set_xlabel('Total Comments')\n",
    "axes[1, 0].set_ylabel('Comments in Peak Period')\n",
    "axes[1, 0].set_title('Peak Comments vs Total Comments')\n",
    "axes[1, 0].set_xscale('log')\n",
    "axes[1, 0].set_yscale('log')\n",
    "axes[1, 0].legend()\n",
    "axes[1, 0].grid(alpha=0.3)\n",
    "\n",
    "# Plot 4: Threshold meeting status\n",
    "threshold_counts = peak_df['meets_threshold'].value_counts()\n",
    "axes[1, 1].bar(['Below 1000', 'Above 1000'], \n",
    "               [threshold_counts.get(False, 0), threshold_counts.get(True, 0)],\n",
    "               color=['lightcoral', 'lightgreen'], edgecolor='black')\n",
    "axes[1, 1].set_ylabel('Number of Shows')\n",
    "axes[1, 1].set_title('Shows Meeting 1000+ Comment Threshold in Peak')\n",
    "axes[1, 1].grid(alpha=0.3, axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{OUTPUT_DIR}/peak_distribution_summary.png\", dpi=150, bbox_inches='tight')\n",
    "print(f\"   Saved summary plots to {OUTPUT_DIR}/peak_distribution_summary.png\")\n",
    "plt.close()\n",
    "\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"Analysis complete!\")\n",
    "print(f\"Check the '{OUTPUT_DIR}' directory for:\")\n",
    "print(f\"  - show_temporal_statistics.csv\")\n",
    "print(f\"  - peak_period_analysis.csv\")\n",
    "print(f\"  - temporal_patterns_sample_shows.png\")\n",
    "print(f\"  - peak_distribution_summary.png\")\n",
    "print(\"=\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5e25b3-0591-48cb-99b7-3929f79c6ff2",
   "metadata": {},
   "source": [
    "## Filter Time Data According to Peak Period Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4aec3449-95cf-43db-9829-cc5bd6a71c71",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Filtering Dataset to Peak Periods\n",
      "================================================================================\n",
      "\n",
      "1. Loading full dataset from Sentiment_Data/Final_InSentiment_Time.csv...\n",
      "   Loaded 2,488,053 rows\n",
      "   Unique shows: 50\n",
      "\n",
      "2. Loading peak period analysis from Sentiment_Data/Temporal_Analysis/peak_period_analysis.csv...\n",
      "   Loaded peak info for 50 shows\n",
      "   Shows with 2000+ comments in peak period: 39\n",
      "   Shows excluded (below threshold): 11\n",
      "\n",
      "   Excluded shows (peak comments < 2000):\n",
      "     - Jujutsu Kaisen: 1880 comments in peak\n",
      "     - Brooklyn Nine-Nine: 1745 comments in peak\n",
      "     - Arrow: 1709 comments in peak\n",
      "     - Riverdale: 1645 comments in peak\n",
      "     - The Big Bang Theory: 1421 comments in peak\n",
      "     - Sweet Home: 1374 comments in peak\n",
      "     - American Horror Story: 1342 comments in peak\n",
      "     - SpongeBob SquarePants: 1042 comments in peak\n",
      "     - South Park: 784 comments in peak\n",
      "     - PAW Patrol: 614 comments in peak\n",
      "     - Crash Landing on You: 229 comments in peak\n",
      "\n",
      "3. Converting timestamps in main dataset...\n",
      "   Warning: 2488004 timestamps failed to parse, trying alternative formats...\n",
      "   Removed 7 rows without valid timestamps\n",
      "   Remaining rows: 2,488,046\n",
      "\n",
      "4. Filtering to peak periods (window size: 28 days)...\n",
      "\n",
      "5. Combining filtered data...\n",
      "   Original dataset: 2,488,046 comments across 50 shows\n",
      "   Filtered dataset: 601,261 comments across 39 shows\n",
      "   Reduction: 75.8%\n",
      "\n",
      "6. Show-level statistics:\n",
      "\n",
      "   Top 10 shows by peak period comments:\n",
      "                SHOW  TOTAL_COMMENTS  PEAK_COMMENTS  PCT_IN_PEAK\n",
      "     Game Of Thrones          248134         118368    47.703257\n",
      "            Euphoria          116428          65963    56.655615\n",
      "     Attack On Titan          199773          39781    19.913101\n",
      "            The Boys           67636          31910    47.179017\n",
      " The Handmaid's Tale          152182          29603    19.452366\n",
      "    Better Call Saul          113205          28919    25.545691\n",
      "     Stranger Things           82115          24900    30.323327\n",
      "                Dark           47624          18834    39.547287\n",
      "The Umbrella Academy           66401          17001    25.603530\n",
      "   The Wheel Of Time           30792          16971    55.114965\n",
      "\n",
      "   Overall statistics:\n",
      "   Mean % of comments in peak: 28.5%\n",
      "   Median % of comments in peak: 20.2%\n",
      "   Min peak comments: 1,969\n",
      "   Max peak comments: 118,368\n",
      "\n",
      "7. Saving filtered dataset...\n",
      "   Saved 601,261 rows to Sentiment_Data/Final_InSentiment_Peaks.csv\n",
      "   Saved show statistics to Sentiment_Data/Final_InSentiment_Peaks_statistics.csv\n",
      "\n",
      "8. Verification:\n",
      "   Columns in filtered dataset: ['Unnamed: 0', 'COMMENT_ID', 'SUBREDDIT', 'SHOW', 'IN_CHAR_CONTEXT', 'OUT_CHAR_CONTEXT', 'OUT_CHARS', 'IN_CHARS', 'IN_CHAR_COUNT', 'OUT_CHAR_COUNT', 'SENTIMENT', 'PREDICTION', 'PREDICTION_2', 'TIME_DATETIME']\n",
      "   Sample of filtered data:\n",
      "              SHOW COMMENT_ID   TIME_DATETIME\n",
      "0  Game Of Thrones    em0qjcp  4/29/2019 0:00\n",
      "1  Game Of Thrones    em0qjcp  4/29/2019 0:00\n",
      "2  Game Of Thrones    em0qkab  4/29/2019 0:00\n",
      "3  Game Of Thrones    em0qphw  4/29/2019 0:02\n",
      "4  Game Of Thrones    em0qphw  4/29/2019 0:02\n",
      "\n",
      "================================================================================\n",
      "Filtering complete!\n",
      "Your peak-period-filtered dataset is ready: Sentiment_Data/Final_InSentiment_Peaks.csv\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import timedelta\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Configuration\n",
    "INPUT_FILE = \"Data/Final_InSentiment_Time.csv\"  # Your full dataset\n",
    "PEAK_ANALYSIS_FILE = \"Data/Temporal_Analysis/peak_period_analysis.csv\"  # From previous analysis\n",
    "OUTPUT_FILE = \"Data/Final_InSentiment_Peaks.csv\"  # Filtered dataset\n",
    "WINDOW_DAYS = 28  # Should match what you used in the analysis\n",
    "MIN_PEAK_COMMENTS = 2000  # Minimum comments required in peak period to include show\n",
    "\n",
    "print(\"=\" * 80)\n",
    "print(\"Filtering Dataset to Peak Periods\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# Load full dataset\n",
    "print(f\"\\n1. Loading full dataset from {INPUT_FILE}...\")\n",
    "df = pd.read_csv(INPUT_FILE)\n",
    "print(f\"   Loaded {len(df):,} rows\")\n",
    "print(f\"   Unique shows: {df['SHOW'].nunique()}\")\n",
    "\n",
    "# Load peak analysis results\n",
    "print(f\"\\n2. Loading peak period analysis from {PEAK_ANALYSIS_FILE}...\")\n",
    "peak_df = pd.read_csv(PEAK_ANALYSIS_FILE)\n",
    "print(f\"   Loaded peak info for {len(peak_df)} shows\")\n",
    "\n",
    "# Convert date columns to datetime\n",
    "peak_df['peak_start'] = pd.to_datetime(peak_df['peak_start'])\n",
    "peak_df['peak_end'] = pd.to_datetime(peak_df['peak_end'])\n",
    "\n",
    "# Filter to shows that meet the threshold\n",
    "shows_meeting_threshold = peak_df[peak_df['actual_comments_in_window'] >= MIN_PEAK_COMMENTS].copy()\n",
    "print(f\"   Shows with {MIN_PEAK_COMMENTS}+ comments in peak period: {len(shows_meeting_threshold)}\")\n",
    "print(f\"   Shows excluded (below threshold): {len(peak_df) - len(shows_meeting_threshold)}\")\n",
    "\n",
    "if len(shows_meeting_threshold) == 0:\n",
    "    print(f\"\\n   ERROR: No shows meet the {MIN_PEAK_COMMENTS} comment threshold!\")\n",
    "    print(\"   Check your MIN_PEAK_COMMENTS value or PEAK_ANALYSIS_FILE\")\n",
    "    exit(1)\n",
    "\n",
    "# Show which shows are being excluded\n",
    "excluded_shows = peak_df[peak_df['actual_comments_in_window'] < MIN_PEAK_COMMENTS].copy()\n",
    "if len(excluded_shows) > 0:\n",
    "    print(f\"\\n   Excluded shows (peak comments < {MIN_PEAK_COMMENTS}):\")\n",
    "    for _, row in excluded_shows.iterrows():\n",
    "        print(f\"     - {row['SHOW']}: {row['actual_comments_in_window']} comments in peak\")\n",
    "\n",
    "# Convert timestamps in main dataset\n",
    "print(f\"\\n3. Converting timestamps in main dataset...\")\n",
    "df['DATETIME'] = pd.to_datetime(df['TIME_DATETIME'], unit='s', errors='coerce')\n",
    "\n",
    "# Handle any failed conversions\n",
    "nat_count = df['DATETIME'].isna().sum()\n",
    "if nat_count > 0:\n",
    "    print(f\"   Warning: {nat_count} timestamps failed to parse, trying alternative formats...\")\n",
    "    mask = df['DATETIME'].isna()\n",
    "    df.loc[mask, 'DATETIME'] = pd.to_datetime(df.loc[mask, 'TIME_DATETIME'], \n",
    "                                                format='mixed', errors='coerce')\n",
    "\n",
    "# Remove rows without valid timestamps\n",
    "before_filter = len(df)\n",
    "df = df[df['DATETIME'].notna()].copy()\n",
    "after_filter = len(df)\n",
    "print(f\"   Removed {before_filter - after_filter:,} rows without valid timestamps\")\n",
    "print(f\"   Remaining rows: {len(df):,}\")\n",
    "\n",
    "# Filter to peak periods\n",
    "print(f\"\\n4. Filtering to peak periods (window size: {WINDOW_DAYS} days)...\")\n",
    "\n",
    "peak_filtered_dfs = []\n",
    "show_stats = []\n",
    "\n",
    "for _, show_info in shows_meeting_threshold.iterrows():\n",
    "    show_name = show_info['SHOW']\n",
    "    peak_start = show_info['peak_start']\n",
    "    peak_end = show_info['peak_end']\n",
    "    \n",
    "    # Get all comments for this show\n",
    "    show_df = df[df['SHOW'] == show_name].copy()\n",
    "    \n",
    "    # Filter to peak period\n",
    "    peak_comments = show_df[\n",
    "        (show_df['DATETIME'] >= peak_start) & \n",
    "        (show_df['DATETIME'] <= peak_end)\n",
    "    ].copy()\n",
    "    \n",
    "    peak_filtered_dfs.append(peak_comments)\n",
    "    \n",
    "    # Track statistics\n",
    "    show_stats.append({\n",
    "        'SHOW': show_name,\n",
    "        'TOTAL_COMMENTS': len(show_df),\n",
    "        'PEAK_COMMENTS': len(peak_comments),\n",
    "        'PCT_IN_PEAK': 100 * len(peak_comments) / len(show_df) if len(show_df) > 0 else 0,\n",
    "        'PEAK_START': peak_start.strftime('%Y-%m-%d'),\n",
    "        'PEAK_END': peak_end.strftime('%Y-%m-%d')\n",
    "    })\n",
    "\n",
    "# Combine all peak period data\n",
    "print(f\"\\n5. Combining filtered data...\")\n",
    "filtered_df = pd.concat(peak_filtered_dfs, ignore_index=True)\n",
    "\n",
    "print(f\"   Original dataset: {len(df):,} comments across {df['SHOW'].nunique()} shows\")\n",
    "print(f\"   Filtered dataset: {len(filtered_df):,} comments across {filtered_df['SHOW'].nunique()} shows\")\n",
    "print(f\"   Reduction: {100 * (1 - len(filtered_df)/len(df)):.1f}%\")\n",
    "\n",
    "# Create statistics summary\n",
    "stats_df = pd.DataFrame(show_stats)\n",
    "stats_df = stats_df.sort_values('PEAK_COMMENTS', ascending=False)\n",
    "\n",
    "print(f\"\\n6. Show-level statistics:\")\n",
    "print(f\"\\n   Top 10 shows by peak period comments:\")\n",
    "print(stats_df[['SHOW', 'TOTAL_COMMENTS', 'PEAK_COMMENTS', 'PCT_IN_PEAK']].head(10).to_string(index=False))\n",
    "\n",
    "print(f\"\\n   Overall statistics:\")\n",
    "print(f\"   Mean % of comments in peak: {stats_df['PCT_IN_PEAK'].mean():.1f}%\")\n",
    "print(f\"   Median % of comments in peak: {stats_df['PCT_IN_PEAK'].median():.1f}%\")\n",
    "print(f\"   Min peak comments: {stats_df['PEAK_COMMENTS'].min():,}\")\n",
    "print(f\"   Max peak comments: {stats_df['PEAK_COMMENTS'].max():,}\")\n",
    "\n",
    "# Save filtered dataset\n",
    "print(f\"\\n7. Saving filtered dataset...\")\n",
    "\n",
    "# Drop the DATETIME column before saving (keep original TIME_DATETIME)\n",
    "filtered_df = filtered_df.drop(columns=['DATETIME'])\n",
    "\n",
    "filtered_df.to_csv(OUTPUT_FILE, index=False)\n",
    "print(f\"   Saved {len(filtered_df):,} rows to {OUTPUT_FILE}\")\n",
    "\n",
    "# Save statistics\n",
    "stats_output = OUTPUT_FILE.replace('.csv', '_statistics.csv')\n",
    "stats_df.to_csv(stats_output, index=False)\n",
    "print(f\"   Saved show statistics to {stats_output}\")\n",
    "\n",
    "# Verify the filtered dataset has the same structure as original\n",
    "print(f\"\\n8. Verification:\")\n",
    "print(f\"   Columns in filtered dataset: {list(filtered_df.columns)}\")\n",
    "print(f\"   Sample of filtered data:\")\n",
    "print(filtered_df[['SHOW', 'COMMENT_ID', 'TIME_DATETIME']].head())\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"Filtering complete!\")\n",
    "print(f\"Your peak-period-filtered dataset is ready: {OUTPUT_FILE}\")\n",
    "print(\"=\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2df3f6-e457-422c-bc63-e3eef9dcfc7c",
   "metadata": {},
   "source": [
    "## Recompute the Predicted Sentiment Percentages and Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "936636d2-c2a9-4248-9bde-ead9b5d5e314",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "GENERATING EMOTION DISTRIBUTION BY SHOW\n",
      "================================================================================\n",
      "\n",
      "Generated emotion distribution for 39 shows\n",
      "Total rows: 995\n",
      "\n",
      "Sample output for '13 Reasons Why':\n",
      "          SHOW            PREDICTION  count  PERCENTAGES\n",
      "13 Reasons Why       [27, 'Neutral']   3487    53.244770\n",
      "13 Reasons Why      [3, 'Annoyance']    477     7.283555\n",
      "13 Reasons Why       [4, 'Approval']    302     4.611391\n",
      "13 Reasons Why   [10, 'Disapproval']    283     4.321270\n",
      "13 Reasons Why     [0, 'Admiration']    241     3.679951\n",
      "13 Reasons Why       [25, 'Sadness']    225     3.435639\n",
      "13 Reasons Why          [2, 'Anger']    222     3.389831\n",
      "13 Reasons Why      [6, 'Confusion']    200     3.053901\n",
      "13 Reasons Why          [18, 'Love']    182     2.779050\n",
      "13 Reasons Why [9, 'Disappointment']    119     1.817071\n",
      "13 Reasons Why      [1, 'Amusement']    108     1.649107\n",
      "13 Reasons Why      [7, 'Curiosity']    100     1.526951\n",
      "13 Reasons Why       [11, 'Disgust']     99     1.511681\n",
      "13 Reasons Why          [14, 'Fear']     67     1.023057\n",
      "13 Reasons Why      [20, 'Optimism']     66     1.007787\n",
      "13 Reasons Why   [22, 'Realization']     64     0.977248\n",
      "13 Reasons Why       [24, 'Remorse']     58     0.885631\n",
      "13 Reasons Why           [17, 'Joy']     57     0.870362\n",
      "13 Reasons Why         [8, 'Desire']     56     0.855092\n",
      "13 Reasons Why      [26, 'Surprise']     49     0.748206\n",
      "13 Reasons Why         [5, 'Caring']     39     0.595511\n",
      "13 Reasons Why    [13, 'Excitement']     18     0.274851\n",
      "13 Reasons Why     [15, 'Gratitude']     18     0.274851\n",
      "13 Reasons Why [12, 'Embarrassment']     10     0.152695\n",
      "13 Reasons Why   [19, 'Nervousness']      2     0.030539\n",
      "\n",
      "✓ Saved emotion distribution to 'Sentiment_Data/Final_InSentiment_ByShow_Peak.csv'\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# STEP 2: GENERATE EMOTION DISTRIBUTION BY SHOW WITH FILTERED TIME DATA\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING EMOTION DISTRIBUTION BY SHOW\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Group by SHOW and PREDICTION_2, count occurrences\n",
    "emotion_counts = filtered_df.groupby(['SHOW', 'PREDICTION_2']).size().reset_index(name='count')\n",
    "\n",
    "# Calculate total counts per show\n",
    "show_totals = filtered_df.groupby('SHOW').size().reset_index(name='total_count')\n",
    "\n",
    "# Merge to get totals\n",
    "emotion_dist = emotion_counts.merge(show_totals, on='SHOW')\n",
    "\n",
    "# Calculate percentages\n",
    "emotion_dist['PERCENTAGES'] = (emotion_dist['count'] / emotion_dist['total_count']) * 100\n",
    "\n",
    "# Drop the total_count column (we don't need it in final output)\n",
    "emotion_dist = emotion_dist[['SHOW', 'PREDICTION_2', 'count', 'PERCENTAGES']]\n",
    "\n",
    "# Rename PREDICTION_2 to PREDICTION for consistency with your format\n",
    "emotion_dist.rename(columns={'PREDICTION_2': 'PREDICTION'}, inplace=True)\n",
    "\n",
    "# Sort by SHOW and count (descending)\n",
    "emotion_dist = emotion_dist.sort_values(['SHOW', 'count'], ascending=[True, False])\n",
    "\n",
    "print(f\"\\nGenerated emotion distribution for {emotion_dist['SHOW'].nunique()} shows\")\n",
    "print(f\"Total rows: {len(emotion_dist)}\")\n",
    "\n",
    "# Display sample for first show\n",
    "first_show = emotion_dist['SHOW'].iloc[0]\n",
    "print(f\"\\nSample output for '{first_show}':\")\n",
    "print(emotion_dist[emotion_dist['SHOW'] == first_show].to_string(index=False))\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE EMOTION DISTRIBUTION\n",
    "# ============================================================================\n",
    "\n",
    "output_dist_file = 'Data/Final_InSentiment_ByShow_Peak.csv'\n",
    "emotion_dist.to_csv(output_dist_file, index=False)\n",
    "print(f\"\\n✓ Saved emotion distribution to '{output_dist_file}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed9ad9fc-2063-4d5b-9648-7838178acfea",
   "metadata": {},
   "source": [
    "## Run Emotion Analysis and other Analytics on the Peak Period Sentiment Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0770f0c5-4ac5-4d79-ab2b-900728ba566e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "EMOTIONAL DIVERSITY ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "Top 15 emotions (by mean % across all shows):\n",
      "   1. [4, 'Approval']                (avg 4.28%)\n",
      "   2. [0, 'Admiration']              (avg 3.90%)\n",
      "   3. [10, 'Disapproval']            (avg 3.45%)\n",
      "   4. [3, 'Annoyance']               (avg 3.29%)\n",
      "   5. [18, 'Love']                   (avg 3.17%)\n",
      "   6. [6, 'Confusion']               (avg 2.86%)\n",
      "   7. [25, 'Sadness']                (avg 2.39%)\n",
      "   8. [1, 'Amusement']               (avg 2.09%)\n",
      "   9. [9, 'Disappointment']          (avg 1.57%)\n",
      "  10. [22, 'Realization']            (avg 1.41%)\n",
      "  11. [2, 'Anger']                   (avg 1.33%)\n",
      "  12. [7, 'Curiosity']               (avg 1.31%)\n",
      "  13. [17, 'Joy']                    (avg 1.19%)\n",
      "  14. [20, 'Optimism']               (avg 1.03%)\n",
      "  15. [8, 'Desire']                  (avg 0.87%)\n",
      "\n",
      "================================================================================\n",
      "DIVERSITY METRICS FOR ALL SHOWS\n",
      "================================================================================\n",
      "\n",
      "Summary Statistics:\n",
      "       shannon_entropy_all  effective_number_all  shannon_entropy_top15  \\\n",
      "count            39.000000             39.000000              39.000000   \n",
      "mean              4.040578             16.485453               3.662760   \n",
      "std               0.087137              0.984511               0.062547   \n",
      "min               3.854542             14.465474               3.492890   \n",
      "25%               3.990206             15.891859               3.636779   \n",
      "50%               4.051857             16.585577               3.669286   \n",
      "75%               4.093502             17.071420               3.707813   \n",
      "max               4.184538             18.183243               3.782524   \n",
      "\n",
      "       effective_number_top15  \n",
      "count               39.000000  \n",
      "mean                12.676356  \n",
      "std                  0.541538  \n",
      "min                 11.258092  \n",
      "25%                 12.438854  \n",
      "50%                 12.722289  \n",
      "75%                 13.066610  \n",
      "max                 13.761100  \n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "INTERPRETATION GUIDE:\n",
      "--------------------------------------------------------------------------------\n",
      "Shannon Entropy (all emotions):\n",
      "  - Theoretical max: ~4.75 (if all 27 non-neutral emotions were equal)\n",
      "  - Higher values = more diverse emotional responses\n",
      "  - Lower values = emotions concentrated in fewer categories\n",
      "\n",
      "Effective Number of Emotions:\n",
      "  - Represents 'how many equally-common emotions' would give same diversity\n",
      "  - E.g., value of 8.5 = equivalent to ~8-9 equally prevalent emotions\n",
      "  - More intuitive than raw entropy\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (All Emotions)\n",
      "================================================================================\n",
      "                           SHOW  total_comments  n_emotions_present  shannon_entropy_all  effective_number_all\n",
      "            The Handmaid's Tale           29603                  25             4.184538             18.183243\n",
      "Marvel's Agents Of S.H.I.E.L.D.            4329                  24             4.183393             18.168826\n",
      "               Better Call Saul           28919                  25             4.182692             18.160001\n",
      "                       Euphoria           65963                  25             4.146968             17.715840\n",
      "                        Lucifer            2497                  25             4.146330             17.708013\n",
      "                Attack On Titan           39781                  26             4.133310             17.548914\n",
      "                The Mandalorian            5865                  24             4.126244             17.463181\n",
      "                Stranger Things           24900                  25             4.122877             17.422461\n",
      "                Game Of Thrones          118368                  26             4.104091             17.197076\n",
      "               The Walking Dead            3060                  23             4.098514             17.130723\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH LOWEST EMOTIONAL DIVERSITY (All Emotions)\n",
      "================================================================================\n",
      "             SHOW  total_comments  n_emotions_present  shannon_entropy_all  effective_number_all\n",
      " My Hero Academia            1969                  24             3.854542             14.465474\n",
      "Dragon Ball Super            3306                  23             3.882098             14.744428\n",
      "          Vikings            2901                  25             3.887094             14.795577\n",
      "        One Piece           13117                  24             3.892763             14.853825\n",
      "        The Flash            1969                  23             3.896554             14.892913\n",
      "        Outlander            2047                  24             3.934920             15.294272\n",
      "   Grey's Anatomy            2483                  23             3.947846             15.431925\n",
      " La Casa De Papel            2341                  24             3.954036             15.498281\n",
      "     Supernatural            3972                  24             3.969172             15.661729\n",
      "             Dark           18834                  25             3.984773             15.832018\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (Top 15 Emotions)\n",
      "================================================================================\n",
      "                 SHOW  total_comments  shannon_entropy_top15  effective_number_top15\n",
      "              Lucifer            2497               3.782524               13.761100\n",
      "      The Mandalorian            5865               3.739121               13.353267\n",
      "     Better Call Saul           28919               3.736026               13.324649\n",
      "  The Handmaid's Tale           29603               3.734119               13.307048\n",
      "      Attack On Titan           39781               3.728948               13.259438\n",
      "      Game Of Thrones          118368               3.728397               13.254379\n",
      "      Stranger Things           24900               3.724878               13.222088\n",
      "The Book Of Boba Fett           12585               3.711945               13.104083\n",
      "           Bridgerton           14252               3.709122               13.078472\n",
      "              The 100           11546               3.708205               13.070163\n",
      "\n",
      "================================================================================\n",
      "SHOWS WITH LOWEST EMOTIONAL DIVERSITY (Top 15 Emotions)\n",
      "================================================================================\n",
      "             SHOW  total_comments  shannon_entropy_top15  effective_number_top15\n",
      " My Hero Academia            1969               3.492890               11.258092\n",
      "          Vikings            2901               3.536309               11.602058\n",
      "        One Piece           13117               3.548109               11.697341\n",
      "        Outlander            2047               3.567130               11.852587\n",
      "Dragon Ball Super            3306               3.575215               11.919200\n",
      "        The Flash            1969               3.586758               12.014945\n",
      "             Dark           18834               3.603331               12.153764\n",
      "   Grey's Anatomy            2483               3.621644               12.309021\n",
      "The Wheel Of Time           16971               3.622156               12.313392\n",
      "   13 Reasons Why            6549               3.634127               12.415991\n",
      "\n",
      "================================================================================\n",
      "COMPLETE DIVERSITY METRICS (sorted by Shannon Entropy All)\n",
      "================================================================================\n",
      "                             SHOW  total_comments  non_neutral_comments  n_emotions_present  shannon_entropy_all  effective_number_all  shannon_entropy_top15  effective_number_top15\n",
      "              The Handmaid's Tale           29603                 11133                  25             4.184538             18.183243               3.734119               13.307048\n",
      "  Marvel's Agents Of S.H.I.E.L.D.            4329                   603                  24             4.183393             18.168826               3.639431               12.461716\n",
      "                 Better Call Saul           28919                  9919                  25             4.182692             18.160001               3.736026               13.324649\n",
      "                         Euphoria           65963                 28488                  25             4.146968             17.715840               3.697835               12.976550\n",
      "                          Lucifer            2497                   964                  25             4.146330             17.708013               3.782524               13.761100\n",
      "                  Attack On Titan           39781                 14128                  26             4.133310             17.548914               3.728948               13.259438\n",
      "                  The Mandalorian            5865                  2033                  24             4.126244             17.463181               3.739121               13.353267\n",
      "                  Stranger Things           24900                 10282                  25             4.122877             17.422461               3.724878               13.222088\n",
      "                  Game Of Thrones          118368                 39362                  26             4.104091             17.197076               3.728397               13.254379\n",
      "                 The Walking Dead            3060                  1247                  23             4.098514             17.130723               3.699071               12.987677\n",
      "                       Squid Game           11268                  4621                  26             4.088491             17.012117               3.663983               12.675605\n",
      "                        Cobra Kai            8245                  3248                  26             4.082864             16.945890               3.683961               12.852357\n",
      "             The Umbrella Academy           17001                  7203                  25             4.079085             16.901562               3.697520               12.973719\n",
      "          Orange Is The New Black            3152                  1714                  25             4.075622             16.861041               3.707421               13.063057\n",
      "                         The Boys           31910                 11785                  25             4.070740             16.804089               3.645075               12.510564\n",
      "                       Bridgerton           14252                  6426                  25             4.066211             16.751412               3.709122               13.078472\n",
      "                   Peaky Blinders            2019                   841                  24             4.064439             16.730851               3.702776               13.021073\n",
      "                        Westworld           13365                  3885                  25             4.053383             16.603130               3.685361               12.864838\n",
      "                          The 100           11546                  4727                  25             4.051896             16.586017               3.708205               13.070163\n",
      "                   13 Reasons Why            6549                  3062                  24             4.051857             16.585577               3.634127               12.415991\n",
      "            The Book Of Boba Fett           12585                  4730                  25             4.050969             16.575365               3.711945               13.104083\n",
      "                      WandaVision           14901                  4500                  24             4.048903             16.551646               3.673131               12.756236\n",
      "                      The Witcher           16684                  6131                  24             4.046851             16.528124               3.656333               12.608575\n",
      "                             Loki           14271                  4735                  25             4.037371             16.419874               3.670313               12.731343\n",
      "                      Moon Knight            5515                  1744                  24             4.031162             16.349356               3.642407               12.487454\n",
      "                   Rick And Morty            4747                  2124                  25             4.021697             16.242446               3.666525               12.697961\n",
      "The Falcon And The Winter Soldier           15592                  5586                  25             4.021031             16.234945               3.667304               12.704821\n",
      "                          Hawkeye            4464                  1645                  23             4.011568             16.128808               3.669286               12.722289\n",
      "                The Wheel Of Time           16971                  5788                  24             3.995638             15.951700               3.622156               12.313392\n",
      "                             Dark           18834                  5712                  25             3.984773             15.832018               3.603331               12.153764\n",
      "                     Supernatural            3972                  1674                  24             3.969172             15.661729               3.649037               12.544970\n",
      "                 La Casa De Papel            2341                  1079                  24             3.954036             15.498281               3.639928               12.466012\n",
      "                   Grey's Anatomy            2483                  1148                  23             3.947846             15.431925               3.621644               12.309021\n",
      "                        Outlander            2047                   882                  24             3.934920             15.294272               3.567130               11.852587\n",
      "                        The Flash            1969                   786                  23             3.896554             14.892913               3.586758               12.014945\n",
      "                        One Piece           13117                  3871                  24             3.892763             14.853825               3.548109               11.697341\n",
      "                          Vikings            2901                  1296                  25             3.887094             14.795577               3.536309               11.602058\n",
      "                Dragon Ball Super            3306                   954                  23             3.882098             14.744428               3.575215               11.919200\n",
      "                 My Hero Academia            1969                   654                  24             3.854542             14.465474               3.492890               11.258092\n",
      "\n",
      "================================================================================\n",
      "RELATIONSHIP BETWEEN SAMPLE SIZE AND DIVERSITY\n",
      "================================================================================\n",
      "\n",
      "Correlation between total comments and Shannon entropy (all emotions): 0.384\n",
      "Correlation between total comments and Shannon entropy (top 15): 0.334\n",
      "\n",
      "Correlation between # emotions present and Shannon entropy: 0.478\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'emotional_diversity_visualizations.png'\n",
      "\n",
      "Results saved to 'Sentiment_Data/Peak_emotional_diversity_results.csv'\n",
      "\n",
      "================================================================================\n",
      "ANALYSIS COMPLETE\n",
      "================================================================================\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import entropy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load your data\n",
    "# Adjust the file path to match your file location and type\n",
    "df = pd.read_csv('Data/Final_InSentiment_ByShow_Peak.csv')  # or read_excel() if it's an Excel file\n",
    "\n",
    "# ============================================================================\n",
    "# DEFINE DIVERSITY METRICS\n",
    "# ============================================================================\n",
    "\n",
    "def calculate_shannon_entropy(show_data, exclude_neutral=True):\n",
    "    \"\"\"\n",
    "    Calculate Shannon entropy for emotion distribution.\n",
    "    Higher values indicate more diverse/spread out emotions.\n",
    "    \n",
    "    Parameters:\n",
    "    - show_data: DataFrame with emotion data for one show\n",
    "    - exclude_neutral: Whether to exclude neutral sentiment (default True)\n",
    "    \n",
    "    Returns:\n",
    "    - Shannon entropy (base 2)\n",
    "    \"\"\"\n",
    "    if exclude_neutral:\n",
    "        # Filter out neutral emotions\n",
    "        data = show_data[~show_data['PREDICTION'].str.contains('Neutral', case=False, na=False)]\n",
    "    else:\n",
    "        data = show_data\n",
    "    \n",
    "    if len(data) == 0 or data['count'].sum() == 0:\n",
    "        return np.nan\n",
    "    \n",
    "    # Get proportions (normalize to sum to 1)\n",
    "    proportions = data['count'] / data['count'].sum()\n",
    "    \n",
    "    # Calculate Shannon entropy (base 2)\n",
    "    H = entropy(proportions, base=2)\n",
    "    \n",
    "    return H\n",
    "\n",
    "\n",
    "def calculate_effective_number(show_data, exclude_neutral=True):\n",
    "    \"\"\"\n",
    "    Calculate effective number of emotions.\n",
    "    This is exp(Shannon entropy) and represents the number of \n",
    "    \"equally common\" emotions that would give the same diversity.\n",
    "    \n",
    "    More interpretable than raw entropy.\n",
    "    \"\"\"\n",
    "    H = calculate_shannon_entropy(show_data, exclude_neutral)\n",
    "    \n",
    "    if np.isnan(H):\n",
    "        return np.nan\n",
    "    \n",
    "    # Effective number = exp(entropy)\n",
    "    # Using base 2 for entropy, so we use 2^H\n",
    "    return 2 ** H\n",
    "\n",
    "\n",
    "def get_top_n_emotions_overall(df, n=15, exclude_neutral=True):\n",
    "    \"\"\"\n",
    "    Get the top N most common emotions across all shows.\n",
    "    \n",
    "    Returns:\n",
    "    - List of emotion names\n",
    "    \"\"\"\n",
    "    if exclude_neutral:\n",
    "        emotion_data = df[~df['PREDICTION'].str.contains('Neutral', case=False, na=False)]\n",
    "    else:\n",
    "        emotion_data = df\n",
    "    \n",
    "    # Calculate mean percentage across all shows for each emotion\n",
    "    top_emotions = (emotion_data.groupby('PREDICTION')['PERCENTAGES']\n",
    "                    .mean()\n",
    "                    .sort_values(ascending=False)\n",
    "                    .head(n)\n",
    "                    .index.tolist())\n",
    "    \n",
    "    return top_emotions\n",
    "\n",
    "\n",
    "# ============================================================================\n",
    "# CALCULATE DIVERSITY METRICS FOR ALL SHOWS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"EMOTIONAL DIVERSITY ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Get top 15 emotions overall\n",
    "top_15_emotions = get_top_n_emotions_overall(df, n=15, exclude_neutral=True)\n",
    "print(f\"\\nTop 15 emotions (by mean % across all shows):\")\n",
    "for i, emotion in enumerate(top_15_emotions, 1):\n",
    "    mean_pct = df[df['PREDICTION'] == emotion]['PERCENTAGES'].mean()\n",
    "    print(f\"  {i:2d}. {emotion:30s} (avg {mean_pct:.2f}%)\")\n",
    "\n",
    "# Calculate diversity metrics for each show\n",
    "diversity_results = []\n",
    "\n",
    "for show in df['SHOW'].unique():\n",
    "    show_data = df[df['SHOW'] == show]\n",
    "    \n",
    "    # Total comments\n",
    "    total_comments = show_data['count'].sum()\n",
    "    \n",
    "    # Non-neutral comments\n",
    "    non_neutral_data = show_data[~show_data['PREDICTION'].str.contains('Neutral', case=False, na=False)]\n",
    "    non_neutral_comments = non_neutral_data['count'].sum()\n",
    "    \n",
    "    # Number of distinct emotions that appear\n",
    "    n_emotions_present = len(non_neutral_data)\n",
    "    \n",
    "    # ALL EMOTIONS METRICS\n",
    "    shannon_all = calculate_shannon_entropy(show_data, exclude_neutral=True)\n",
    "    effective_all = calculate_effective_number(show_data, exclude_neutral=True)\n",
    "    \n",
    "    # TOP 15 EMOTIONS METRICS\n",
    "    top15_data = show_data[show_data['PREDICTION'].isin(top_15_emotions)]\n",
    "    shannon_top15 = calculate_shannon_entropy(top15_data, exclude_neutral=False)  # Already filtered\n",
    "    effective_top15 = calculate_effective_number(top15_data, exclude_neutral=False)\n",
    "    \n",
    "    diversity_results.append({\n",
    "        'SHOW': show,\n",
    "        'total_comments': total_comments,\n",
    "        'non_neutral_comments': non_neutral_comments,\n",
    "        'n_emotions_present': n_emotions_present,\n",
    "        'shannon_entropy_all': shannon_all,\n",
    "        'effective_number_all': effective_all,\n",
    "        'shannon_entropy_top15': shannon_top15,\n",
    "        'effective_number_top15': effective_top15\n",
    "    })\n",
    "\n",
    "diversity_df = pd.DataFrame(diversity_results).sort_values('shannon_entropy_all', ascending=False)\n",
    "\n",
    "# ============================================================================\n",
    "# DISPLAY RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"DIVERSITY METRICS FOR ALL SHOWS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nSummary Statistics:\")\n",
    "print(diversity_df[['shannon_entropy_all', 'effective_number_all', \n",
    "                    'shannon_entropy_top15', 'effective_number_top15']].describe())\n",
    "\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "print(\"INTERPRETATION GUIDE:\")\n",
    "print(\"-\"*80)\n",
    "print(\"Shannon Entropy (all emotions):\")\n",
    "print(\"  - Theoretical max: ~4.75 (if all 27 non-neutral emotions were equal)\")\n",
    "print(\"  - Higher values = more diverse emotional responses\")\n",
    "print(\"  - Lower values = emotions concentrated in fewer categories\")\n",
    "print(\"\\nEffective Number of Emotions:\")\n",
    "print(\"  - Represents 'how many equally-common emotions' would give same diversity\")\n",
    "print(\"  - E.g., value of 8.5 = equivalent to ~8-9 equally prevalent emotions\")\n",
    "print(\"  - More intuitive than raw entropy\")\n",
    "print(\"-\"*80)\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (All Emotions)\")\n",
    "print(\"=\"*80)\n",
    "top_diverse = diversity_df.nlargest(10, 'effective_number_all')[\n",
    "    ['SHOW', 'total_comments', 'n_emotions_present', \n",
    "     'shannon_entropy_all', 'effective_number_all']\n",
    "]\n",
    "print(top_diverse.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH LOWEST EMOTIONAL DIVERSITY (All Emotions)\")\n",
    "print(\"=\"*80)\n",
    "bottom_diverse = diversity_df.nsmallest(10, 'effective_number_all')[\n",
    "    ['SHOW', 'total_comments', 'n_emotions_present', \n",
    "     'shannon_entropy_all', 'effective_number_all']\n",
    "]\n",
    "print(bottom_diverse.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH HIGHEST EMOTIONAL DIVERSITY (Top 15 Emotions)\")\n",
    "print(\"=\"*80)\n",
    "top_diverse_15 = diversity_df.nlargest(10, 'effective_number_top15')[\n",
    "    ['SHOW', 'total_comments', 'shannon_entropy_top15', 'effective_number_top15']\n",
    "]\n",
    "print(top_diverse_15.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SHOWS WITH LOWEST EMOTIONAL DIVERSITY (Top 15 Emotions)\")\n",
    "print(\"=\"*80)\n",
    "bottom_diverse_15 = diversity_df.nsmallest(10, 'effective_number_top15')[\n",
    "    ['SHOW', 'total_comments', 'shannon_entropy_top15', 'effective_number_top15']\n",
    "]\n",
    "print(bottom_diverse_15.to_string(index=False))\n",
    "\n",
    "# ============================================================================\n",
    "# FULL DATASET\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPLETE DIVERSITY METRICS (sorted by Shannon Entropy All)\")\n",
    "print(\"=\"*80)\n",
    "print(diversity_df.to_string(index=False))\n",
    "\n",
    "# ============================================================================\n",
    "# RELATIONSHIP ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"RELATIONSHIP BETWEEN SAMPLE SIZE AND DIVERSITY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Correlation between sample size and diversity\n",
    "corr_all = diversity_df['total_comments'].corr(diversity_df['shannon_entropy_all'])\n",
    "corr_top15 = diversity_df['total_comments'].corr(diversity_df['shannon_entropy_top15'])\n",
    "\n",
    "print(f\"\\nCorrelation between total comments and Shannon entropy (all emotions): {corr_all:.3f}\")\n",
    "print(f\"Correlation between total comments and Shannon entropy (top 15): {corr_top15:.3f}\")\n",
    "\n",
    "# Correlation between number of emotions present and diversity\n",
    "corr_n_emotions = diversity_df['n_emotions_present'].corr(diversity_df['shannon_entropy_all'])\n",
    "print(f\"\\nCorrelation between # emotions present and Shannon entropy: {corr_n_emotions:.3f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "\n",
    "# Plot 1: Distribution of Shannon entropy (all emotions)\n",
    "ax1 = axes[0, 0]\n",
    "diversity_df['shannon_entropy_all'].hist(bins=20, ax=ax1, edgecolor='black', color='steelblue')\n",
    "ax1.set_xlabel('Shannon Entropy (All Emotions)')\n",
    "ax1.set_ylabel('Number of Shows')\n",
    "ax1.set_title('Distribution of Emotional Diversity\\n(All Non-Neutral Emotions)')\n",
    "ax1.axvline(diversity_df['shannon_entropy_all'].mean(), color='red', \n",
    "            linestyle='--', linewidth=2, label=f'Mean: {diversity_df[\"shannon_entropy_all\"].mean():.2f}')\n",
    "ax1.axvline(diversity_df['shannon_entropy_all'].median(), color='orange', \n",
    "            linestyle='--', linewidth=2, label=f'Median: {diversity_df[\"shannon_entropy_all\"].median():.2f}')\n",
    "ax1.legend()\n",
    "\n",
    "# Plot 2: Distribution of effective number (all emotions)\n",
    "ax2 = axes[0, 1]\n",
    "diversity_df['effective_number_all'].hist(bins=20, ax=ax2, edgecolor='black', color='coral')\n",
    "ax2.set_xlabel('Effective Number of Emotions')\n",
    "ax2.set_ylabel('Number of Shows')\n",
    "ax2.set_title('Distribution of Effective Number of Emotions\\n(All Non-Neutral Emotions)')\n",
    "ax2.axvline(diversity_df['effective_number_all'].mean(), color='red', \n",
    "            linestyle='--', linewidth=2, label=f'Mean: {diversity_df[\"effective_number_all\"].mean():.2f}')\n",
    "ax2.axvline(diversity_df['effective_number_all'].median(), color='orange', \n",
    "            linestyle='--', linewidth=2, label=f'Median: {diversity_df[\"effective_number_all\"].median():.2f}')\n",
    "ax2.legend()\n",
    "\n",
    "# Plot 3: Sample size vs diversity (all emotions)\n",
    "ax3 = axes[1, 0]\n",
    "ax3.scatter(diversity_df['total_comments'], diversity_df['shannon_entropy_all'], \n",
    "            alpha=0.6, s=50)\n",
    "ax3.set_xlabel('Total Comments (log scale)')\n",
    "ax3.set_ylabel('Shannon Entropy (All Emotions)')\n",
    "ax3.set_title('Sample Size vs Emotional Diversity')\n",
    "ax3.set_xscale('log')\n",
    "# Add trend line\n",
    "z = np.polyfit(np.log10(diversity_df['total_comments']), \n",
    "               diversity_df['shannon_entropy_all'], 1)\n",
    "p = np.poly1d(z)\n",
    "x_trend = np.logspace(np.log10(diversity_df['total_comments'].min()), \n",
    "                      np.log10(diversity_df['total_comments'].max()), 100)\n",
    "ax3.plot(x_trend, p(np.log10(x_trend)), \"r--\", alpha=0.8, \n",
    "         label=f'r = {corr_all:.3f}')\n",
    "ax3.legend()\n",
    "\n",
    "# Plot 4: Comparison of all emotions vs top 15\n",
    "ax4 = axes[1, 1]\n",
    "ax4.scatter(diversity_df['effective_number_all'], \n",
    "            diversity_df['effective_number_top15'], \n",
    "            alpha=0.6, s=50, color='purple')\n",
    "ax4.set_xlabel('Effective Number (All Emotions)')\n",
    "ax4.set_ylabel('Effective Number (Top 15 Emotions)')\n",
    "ax4.set_title('Diversity: All Emotions vs Top 15 Only')\n",
    "# Add diagonal line\n",
    "max_val = max(diversity_df['effective_number_all'].max(), \n",
    "              diversity_df['effective_number_top15'].max())\n",
    "ax4.plot([0, max_val], [0, max_val], 'k--', alpha=0.3, label='y=x')\n",
    "ax4.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Figures/Peak_emotional_diversity_visualizations.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'emotional_diversity_visualizations.png'\")\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE RESULTS TO CSV\n",
    "# ============================================================================\n",
    "\n",
    "output_file = 'Data/Peak_emotional_diversity_results.csv'\n",
    "diversity_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nResults saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b352695f-552d-41fa-bc21-26c06f2bcd5d",
   "metadata": {},
   "source": [
    "## Outlier Analysis (based on correlation between sample size and emotional diversity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "92452c65-c3bc-4784-9132-831c37b8e9ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "OUTLIER ANALYSIS: SAMPLE SIZE vs EMOTIONAL DIVERSITY\n",
      "================================================================================\n",
      "\n",
      "Focusing on TOP 15 EMOTIONS for reliable outlier detection\n",
      "(Rare emotions excluded due to high variance)\n",
      "\n",
      "================================================================================\n",
      "OUTLIER DEFINITIONS\n",
      "================================================================================\n",
      "\n",
      "Residual = Actual diversity - Predicted diversity (based on sample size)\n",
      "  Positive residual = MORE diverse than expected for its sample size\n",
      "  Negative residual = LESS diverse than expected for its sample size\n",
      "\n",
      "Z-score thresholds:\n",
      "  |z| > 2.0 = Notable outlier (top/bottom ~5%)\n",
      "  |z| > 1.5 = Moderate outlier (top/bottom ~13%)\n",
      "\n",
      "================================================================================\n",
      "OUTLIERS: MUCH MORE DIVERSE THAN EXPECTED (Top 15 Emotions, z > 2.0)\n",
      "================================================================================\n",
      "These shows have unusually diverse emotional responses for their sample size\n",
      "(Based on top 15 most reliable emotions)\n",
      "\n",
      "   SHOW  total_comments  shannon_entropy_top15  predicted_diversity_top15  residual_top15  residual_zscore_top15\n",
      "Lucifer            2497               3.782524                   3.631955        0.150569               2.653055\n",
      "\n",
      "================================================================================\n",
      "OUTLIERS: MUCH LESS DIVERSE THAN EXPECTED (Top 15 Emotions, z < -2.0)\n",
      "================================================================================\n",
      "These shows have unusually concentrated emotional responses for their sample size\n",
      "(Based on top 15 most reliable emotions)\n",
      "\n",
      "            SHOW  total_comments  shannon_entropy_top15  predicted_diversity_top15  residual_top15  residual_zscore_top15\n",
      "My Hero Academia            1969               3.492890                   3.626066       -0.133176              -2.346582\n",
      "       One Piece           13117               3.548109                   3.673069       -0.124960              -2.201817\n",
      "\n",
      "================================================================================\n",
      "COMPARISON: TOP 15 vs ALL EMOTIONS OUTLIER DETECTION\n",
      "================================================================================\n",
      "\n",
      "Outliers detected (|z| > 1.5):\n",
      "  Using top 15 emotions: 5 shows\n",
      "  Using all emotions: 5 shows\n",
      "  In both: 3 shows\n",
      "  Only in top 15: 2 shows\n",
      "  Only in all: 2 shows\n",
      "\n",
      "Shows that are outliers with 'all emotions' but NOT with top 15:\n",
      "(These may be outliers due to rare emotion noise)\n",
      "  - Marvel's Agents Of S.H.I.E.L.D.: z_all=2.30, z_top15=-0.11\n",
      "  - Dragon Ball Super: z_all=-1.58, z_top15=-1.12\n",
      "\n",
      "================================================================================\n",
      "SUMMARY OF OUTLIER CATEGORIES (based on Top 15)\n",
      "================================================================================\n",
      "\n",
      "Distribution of shows by outlier category:\n",
      "outlier_category\n",
      "Less diverse          1\n",
      "More diverse          1\n",
      "Much LESS diverse     2\n",
      "Much MORE diverse     1\n",
      "Normal               34\n",
      "Name: count, dtype: int64\n",
      "\n",
      "================================================================================\n",
      "INTERESTING PATTERNS (based on Top 15)\n",
      "================================================================================\n",
      "\n",
      "Small sample size BUT high diversity:\n",
      "(Fewer comments than median, but more diverse than median)\n",
      "\n",
      "                   SHOW  total_comments  effective_number_top15  residual_zscore_top15\n",
      "                Lucifer            2497               13.761100               2.653055\n",
      "        The Mandalorian            5865               13.353267               1.515366\n",
      "Orange Is The New Black            3152               13.063057               1.227991\n",
      "         Peaky Blinders            2019               13.021073               1.340690\n",
      "       The Walking Dead            3060               12.987677               1.093814\n",
      "              Cobra Kai            8245               12.852357               0.394694\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Large sample size BUT low diversity:\n",
      "(More comments than median, but less diverse than median)\n",
      "\n",
      "                             SHOW  total_comments  effective_number_top15  residual_zscore_top15\n",
      "                        One Piece           13117               11.697341              -2.201817\n",
      "                             Dark           18834               12.153764              -1.386770\n",
      "                The Wheel Of Time           16971               12.313392              -1.009582\n",
      "                         The Boys           31910               12.510564              -0.881505\n",
      "                      The Witcher           16684               12.608575              -0.399930\n",
      "The Falcon And The Winter Soldier           15592               12.704821              -0.177060\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'diversity_outliers_top15_analysis.png'\n",
      "Saving residual outliers plot as separate image...\n",
      "Standalone residual plot saved as 'diversity_outliers_residual_plot.png'\n",
      "\n",
      "Extended results (with residuals and outlier categories) saved to 'Sentiment_Data/Peak_diversity_with_outliers_top15.csv'\n",
      "\n",
      "================================================================================\n",
      "OUTLIER ANALYSIS COMPLETE\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Load the diversity results from previous analysis\n",
    "# If you saved it as CSV:\n",
    "diversity_df = pd.read_csv('Data/Peak_emotional_diversity_results.csv')\n",
    "\n",
    "# ============================================================================\n",
    "# CALCULATE RESIDUALS FROM EXPECTED RELATIONSHIP\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS: SAMPLE SIZE vs EMOTIONAL DIVERSITY\")\n",
    "print(\"=\"*80)\n",
    "print(\"\\nFocusing on TOP 15 EMOTIONS for reliable outlier detection\")\n",
    "print(\"(Rare emotions excluded due to high variance)\")\n",
    "\n",
    "# Calculate regression line: diversity ~ log(sample_size)\n",
    "# Using log because the relationship appears nonlinear\n",
    "diversity_df['log_comments'] = np.log10(diversity_df['total_comments'])\n",
    "\n",
    "# PRIMARY ANALYSIS: Top 15 emotions (most reliable)\n",
    "slope_top15, intercept_top15, r_top15, p_top15, se_top15 = stats.linregress(\n",
    "    diversity_df['log_comments'], \n",
    "    diversity_df['shannon_entropy_top15']\n",
    ")\n",
    "\n",
    "diversity_df['predicted_diversity_top15'] = (\n",
    "    slope_top15 * diversity_df['log_comments'] + intercept_top15\n",
    ")\n",
    "\n",
    "diversity_df['residual_top15'] = (\n",
    "    diversity_df['shannon_entropy_top15'] - diversity_df['predicted_diversity_top15']\n",
    ")\n",
    "\n",
    "diversity_df['residual_zscore_top15'] = (\n",
    "    (diversity_df['residual_top15'] - diversity_df['residual_top15'].mean()) / \n",
    "    diversity_df['residual_top15'].std()\n",
    ")\n",
    "\n",
    "# SECONDARY ANALYSIS: All emotions (for comparison)\n",
    "slope_all, intercept_all, r_all, p_all, se_all = stats.linregress(\n",
    "    diversity_df['log_comments'], \n",
    "    diversity_df['shannon_entropy_all']\n",
    ")\n",
    "\n",
    "diversity_df['predicted_diversity_all'] = (\n",
    "    slope_all * diversity_df['log_comments'] + intercept_all\n",
    ")\n",
    "\n",
    "diversity_df['residual_all'] = (\n",
    "    diversity_df['shannon_entropy_all'] - diversity_df['predicted_diversity_all']\n",
    ")\n",
    "\n",
    "diversity_df['residual_zscore_all'] = (\n",
    "    (diversity_df['residual_all'] - diversity_df['residual_all'].mean()) / \n",
    "    diversity_df['residual_all'].std()\n",
    ")\n",
    "\n",
    "# ============================================================================\n",
    "# IDENTIFY OUTLIERS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIER DEFINITIONS\")\n",
    "print(\"=\"*80)\n",
    "print(\"\\nResidual = Actual diversity - Predicted diversity (based on sample size)\")\n",
    "print(\"  Positive residual = MORE diverse than expected for its sample size\")\n",
    "print(\"  Negative residual = LESS diverse than expected for its sample size\")\n",
    "print(\"\\nZ-score thresholds:\")\n",
    "print(\"  |z| > 2.0 = Notable outlier (top/bottom ~5%)\")\n",
    "print(\"  |z| > 1.5 = Moderate outlier (top/bottom ~13%)\")\n",
    "\n",
    "# Define outlier thresholds\n",
    "threshold_strong = 2.0\n",
    "threshold_moderate = 1.5\n",
    "\n",
    "# Categorize outliers based on TOP 15 (primary analysis)\n",
    "diversity_df['outlier_category'] = 'Normal'\n",
    "diversity_df.loc[diversity_df['residual_zscore_top15'] > threshold_strong, 'outlier_category'] = 'Much MORE diverse'\n",
    "diversity_df.loc[(diversity_df['residual_zscore_top15'] > threshold_moderate) & \n",
    "                 (diversity_df['residual_zscore_top15'] <= threshold_strong), 'outlier_category'] = 'More diverse'\n",
    "diversity_df.loc[diversity_df['residual_zscore_top15'] < -threshold_strong, 'outlier_category'] = 'Much LESS diverse'\n",
    "diversity_df.loc[(diversity_df['residual_zscore_top15'] < -threshold_moderate) & \n",
    "                 (diversity_df['residual_zscore_top15'] >= -threshold_strong), 'outlier_category'] = 'Less diverse'\n",
    "\n",
    "# ============================================================================\n",
    "# DISPLAY OUTLIERS - TOP 15 EMOTIONS (PRIMARY)\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIERS: MUCH MORE DIVERSE THAN EXPECTED (Top 15 Emotions, z > 2.0)\")\n",
    "print(\"=\"*80)\n",
    "print(\"These shows have unusually diverse emotional responses for their sample size\")\n",
    "print(\"(Based on top 15 most reliable emotions)\\n\")\n",
    "\n",
    "high_outliers = diversity_df[diversity_df['residual_zscore_top15'] > threshold_strong].sort_values(\n",
    "    'residual_zscore_top15', ascending=False\n",
    ")\n",
    "\n",
    "if len(high_outliers) > 0:\n",
    "    print(high_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                          'predicted_diversity_top15', 'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows exceed z > 2.0 threshold\")\n",
    "    print(\"\\nMost diverse relative to size (z > 1.5):\")\n",
    "    high_outliers_moderate = diversity_df[diversity_df['residual_zscore_top15'] > threshold_moderate].sort_values(\n",
    "        'residual_zscore_top15', ascending=False\n",
    "    )\n",
    "    if len(high_outliers_moderate) > 0:\n",
    "        print(high_outliers_moderate[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                                       'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"No outliers found at z > 1.5 threshold either\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIERS: MUCH LESS DIVERSE THAN EXPECTED (Top 15 Emotions, z < -2.0)\")\n",
    "print(\"=\"*80)\n",
    "print(\"These shows have unusually concentrated emotional responses for their sample size\")\n",
    "print(\"(Based on top 15 most reliable emotions)\\n\")\n",
    "\n",
    "low_outliers = diversity_df[diversity_df['residual_zscore_top15'] < -threshold_strong].sort_values(\n",
    "    'residual_zscore_top15'\n",
    ")\n",
    "\n",
    "if len(low_outliers) > 0:\n",
    "    print(low_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                         'predicted_diversity_top15', 'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows exceed z < -2.0 threshold\")\n",
    "    print(\"\\nLeast diverse relative to size (z < -1.5):\")\n",
    "    low_outliers_moderate = diversity_df[diversity_df['residual_zscore_top15'] < -threshold_moderate].sort_values(\n",
    "        'residual_zscore_top15'\n",
    "    )\n",
    "    if len(low_outliers_moderate) > 0:\n",
    "        print(low_outliers_moderate[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                                      'residual_top15', 'residual_zscore_top15']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"No outliers found at z < -1.5 threshold either\")\n",
    "\n",
    "# ============================================================================\n",
    "# COMPARISON: TOP 15 vs ALL EMOTIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPARISON: TOP 15 vs ALL EMOTIONS OUTLIER DETECTION\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Shows that are outliers in one but not the other\n",
    "top15_outliers = set(diversity_df[abs(diversity_df['residual_zscore_top15']) > threshold_moderate]['SHOW'])\n",
    "all_outliers = set(diversity_df[abs(diversity_df['residual_zscore_all']) > threshold_moderate]['SHOW'])\n",
    "\n",
    "only_top15 = top15_outliers - all_outliers\n",
    "only_all = all_outliers - top15_outliers\n",
    "both = top15_outliers & all_outliers\n",
    "\n",
    "print(f\"\\nOutliers detected (|z| > 1.5):\")\n",
    "print(f\"  Using top 15 emotions: {len(top15_outliers)} shows\")\n",
    "print(f\"  Using all emotions: {len(all_outliers)} shows\")\n",
    "print(f\"  In both: {len(both)} shows\")\n",
    "print(f\"  Only in top 15: {len(only_top15)} shows\")\n",
    "print(f\"  Only in all: {len(only_all)} shows\")\n",
    "\n",
    "if len(only_all) > 0:\n",
    "    print(f\"\\nShows that are outliers with 'all emotions' but NOT with top 15:\")\n",
    "    print(\"(These may be outliers due to rare emotion noise)\")\n",
    "    for show in only_all:\n",
    "        z_all = diversity_df.loc[diversity_df['SHOW'] == show, 'residual_zscore_all'].values[0]\n",
    "        z_top15 = diversity_df.loc[diversity_df['SHOW'] == show, 'residual_zscore_top15'].values[0]\n",
    "        print(f\"  - {show}: z_all={z_all:.2f}, z_top15={z_top15:.2f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# GENRE/TYPE ANALYSIS OF OUTLIERS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SUMMARY OF OUTLIER CATEGORIES (based on Top 15)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "outlier_summary = diversity_df['outlier_category'].value_counts().sort_index()\n",
    "print(\"\\nDistribution of shows by outlier category:\")\n",
    "print(outlier_summary)\n",
    "\n",
    "# ============================================================================\n",
    "# INTERESTING CASES: SPECIFIC PATTERNS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"INTERESTING PATTERNS (based on Top 15)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Small sample, high diversity\n",
    "small_diverse = diversity_df[\n",
    "    (diversity_df['total_comments'] < diversity_df['total_comments'].median()) &\n",
    "    (diversity_df['shannon_entropy_top15'] > diversity_df['shannon_entropy_top15'].median())\n",
    "].sort_values('effective_number_top15', ascending=False)\n",
    "\n",
    "print(\"\\nSmall sample size BUT high diversity:\")\n",
    "print(\"(Fewer comments than median, but more diverse than median)\\n\")\n",
    "if len(small_diverse) > 0:\n",
    "    print(small_diverse[['SHOW', 'total_comments', 'effective_number_top15', \n",
    "                         'residual_zscore_top15']].head(10).to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows match this pattern\")\n",
    "\n",
    "# Large sample, low diversity\n",
    "large_concentrated = diversity_df[\n",
    "    (diversity_df['total_comments'] > diversity_df['total_comments'].median()) &\n",
    "    (diversity_df['shannon_entropy_top15'] < diversity_df['shannon_entropy_top15'].median())\n",
    "].sort_values('effective_number_top15')\n",
    "\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "print(\"\\nLarge sample size BUT low diversity:\")\n",
    "print(\"(More comments than median, but less diverse than median)\\n\")\n",
    "if len(large_concentrated) > 0:\n",
    "    print(large_concentrated[['SHOW', 'total_comments', 'effective_number_top15', \n",
    "                              'residual_zscore_top15']].head(10).to_string(index=False))\n",
    "else:\n",
    "    print(\"No shows match this pattern\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "\n",
    "# Plot 1: Residual plot with outliers highlighted (TOP 15)\n",
    "ax1 = axes[0, 0]\n",
    "colors = {'Normal': 'gray', 'More diverse': 'lightblue', 'Much MORE diverse': 'darkblue',\n",
    "          'Less diverse': 'lightcoral', 'Much LESS diverse': 'darkred'}\n",
    "for category in diversity_df['outlier_category'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category'] == category]\n",
    "    ax1.scatter(subset['total_comments'], subset['residual_top15'], \n",
    "               label=category, alpha=0.7, s=60, color=colors.get(category, 'gray'))\n",
    "\n",
    "ax1.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.5)\n",
    "ax1.axhline(threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='blue', linestyle=':', alpha=0.5)\n",
    "ax1.axhline(-threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='red', linestyle=':', alpha=0.5)\n",
    "ax1.set_xlabel('Total Comments (log scale)')\n",
    "ax1.set_ylabel('Residual (Actual - Predicted Diversity)')\n",
    "ax1.set_title('Outliers: Diversity vs Sample Size\\n(Top 15 Emotions)')\n",
    "ax1.set_xscale('log')\n",
    "ax1.legend(loc='best', fontsize=8)\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Add labels for outliers (moderate and strong) and Squid Game\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    # Label all outliers (Much MORE/LESS and More/Less diverse)\n",
    "    if row['outlier_category'] != 'Normal':\n",
    "        ax1.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=7, alpha=0.8)\n",
    "    # Also label Squid Game if it's not already labeled\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax1.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=8, alpha=0.9, fontweight='bold')\n",
    "\n",
    "# Plot 2: Distribution of residuals (TOP 15)\n",
    "ax2 = axes[0, 1]\n",
    "ax2.hist(diversity_df['residual_top15'], bins=20, edgecolor='black', alpha=0.7, color='steelblue')\n",
    "ax2.axvline(0, color='black', linestyle='--', linewidth=2)\n",
    "ax2.axvline(threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='blue', linestyle=':', linewidth=2, label='±1.5 SD')\n",
    "ax2.axvline(-threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='red', linestyle=':', linewidth=2)\n",
    "ax2.set_xlabel('Residual (Actual - Predicted)')\n",
    "ax2.set_ylabel('Number of Shows')\n",
    "ax2.set_title('Distribution of Residuals\\n(Top 15 Emotions)')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 3: Actual vs Predicted with regression line (TOP 15)\n",
    "ax3 = axes[0, 2]\n",
    "ax3.scatter(diversity_df['log_comments'], diversity_df['shannon_entropy_top15'], \n",
    "           alpha=0.6, s=50, label='Actual', color='steelblue')\n",
    "# Regression line\n",
    "x_line = np.linspace(diversity_df['log_comments'].min(), \n",
    "                     diversity_df['log_comments'].max(), 100)\n",
    "y_line = slope_top15 * x_line + intercept_top15\n",
    "ax3.plot(x_line, y_line, 'r--', linewidth=2, \n",
    "        label=f'Predicted (r={r_top15:.3f})')\n",
    "ax3.set_xlabel('Log10(Total Comments)')\n",
    "ax3.set_ylabel('Shannon Entropy (Top 15 Emotions)')\n",
    "ax3.set_title('Actual vs Predicted Diversity\\n(Top 15 Emotions)')\n",
    "ax3.legend()\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Highlight outliers\n",
    "outlier_mask = abs(diversity_df['residual_zscore_top15']) > threshold_moderate\n",
    "ax3.scatter(diversity_df.loc[outlier_mask, 'log_comments'],\n",
    "           diversity_df.loc[outlier_mask, 'shannon_entropy_top15'],\n",
    "           s=100, facecolors='none', edgecolors='red', linewidths=2,\n",
    "           label='Outliers', zorder=5)\n",
    "\n",
    "# Plot 4: Q-Q plot for residuals (TOP 15)\n",
    "ax4 = axes[1, 0]\n",
    "stats.probplot(diversity_df['residual_top15'], dist=\"norm\", plot=ax4)\n",
    "ax4.set_title('Q-Q Plot: Normality of Residuals\\n(Top 15 Emotions)')\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 5: Comparison of z-scores (Top 15 vs All)\n",
    "ax5 = axes[1, 1]\n",
    "ax5.scatter(diversity_df['residual_zscore_all'], diversity_df['residual_zscore_top15'], \n",
    "           alpha=0.6, s=50)\n",
    "ax5.plot([-3, 3], [-3, 3], 'k--', alpha=0.3, label='y=x')\n",
    "ax5.axhline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax5.axvline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax5.set_xlabel('Z-score (All Emotions)')\n",
    "ax5.set_ylabel('Z-score (Top 15 Emotions)')\n",
    "ax5.set_title('Comparison: Top 15 vs All Emotions\\nOutlier Detection')\n",
    "ax5.legend()\n",
    "ax5.grid(True, alpha=0.3)\n",
    "\n",
    "# Highlight shows that differ significantly\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    z_diff = abs(row['residual_zscore_all'] - row['residual_zscore_top15'])\n",
    "    if z_diff > 1.0:  # Significant difference\n",
    "        ax5.annotate(row['SHOW'], \n",
    "                    (row['residual_zscore_all'], row['residual_zscore_top15']),\n",
    "                    fontsize=6, alpha=0.7)\n",
    "\n",
    "# Plot 6: Correlation between top 15 and all emotions diversity\n",
    "ax6 = axes[1, 2]\n",
    "ax6.scatter(diversity_df['shannon_entropy_all'], diversity_df['shannon_entropy_top15'],\n",
    "           alpha=0.6, s=50, color='purple')\n",
    "corr = diversity_df['shannon_entropy_all'].corr(diversity_df['shannon_entropy_top15'])\n",
    "ax6.set_xlabel('Shannon Entropy (All Emotions)')\n",
    "ax6.set_ylabel('Shannon Entropy (Top 15 Emotions)')\n",
    "ax6.set_title(f'Diversity: All vs Top 15 Emotions\\n(correlation: {corr:.3f})')\n",
    "ax6.grid(True, alpha=0.3)\n",
    "\n",
    "# Add diagonal\n",
    "min_val = min(diversity_df['shannon_entropy_all'].min(), diversity_df['shannon_entropy_top15'].min())\n",
    "max_val = max(diversity_df['shannon_entropy_all'].max(), diversity_df['shannon_entropy_top15'].max())\n",
    "ax6.plot([min_val, max_val], [min_val, max_val], 'k--', alpha=0.3, label='y=x')\n",
    "ax6.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Figures/Peak_diversity_outliers_top15_analysis.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'diversity_outliers_top15_analysis.png'\")\n",
    "\n",
    "# Save Plot 1 separately as a standalone image\n",
    "print(\"Saving residual outliers plot as separate image...\")\n",
    "fig_standalone = plt.figure(figsize=(12, 8))\n",
    "ax_standalone = fig_standalone.add_subplot(111)\n",
    "\n",
    "# Recreate plot 1 with same styling\n",
    "for category in diversity_df['outlier_category'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category'] == category]\n",
    "    ax_standalone.scatter(subset['total_comments'], subset['residual_top15'], \n",
    "               label=category, alpha=0.7, s=80, color=colors.get(category, 'gray'))\n",
    "\n",
    "ax_standalone.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.5)\n",
    "ax_standalone.axhline(threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='blue', linestyle=':', alpha=0.5)\n",
    "ax_standalone.axhline(-threshold_moderate * diversity_df['residual_top15'].std(), \n",
    "           color='red', linestyle=':', alpha=0.5)\n",
    "ax_standalone.set_xlabel('Total Comments (log scale)', fontsize=12)\n",
    "ax_standalone.set_ylabel('Residual (Actual - Predicted Diversity)', fontsize=12)\n",
    "ax_standalone.set_title('Emotional Diversity Outliers vs Sample Size\\n(Top 15 Emotions)', fontsize=14)\n",
    "ax_standalone.set_xscale('log')\n",
    "ax_standalone.legend(loc='best', fontsize=10)\n",
    "ax_standalone.grid(True, alpha=0.3)\n",
    "\n",
    "# Add labels for outliers and Squid Game\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    # Label all outliers (Much MORE/LESS and More/Less diverse)\n",
    "    if row['outlier_category'] != 'Normal':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=9, alpha=0.85)\n",
    "    # Also label Squid Game if it's not already labeled\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['residual_top15']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=10, alpha=0.95, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Figures/Peak_diversity_outliers_residual_plot.png', dpi=300, bbox_inches='tight')\n",
    "print(\"Standalone residual plot saved as 'diversity_outliers_residual_plot.png'\")\n",
    "plt.close(fig_standalone)\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE EXTENDED RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "output_file = 'Data/Peak_diversity_with_outliers_top15.csv'\n",
    "diversity_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nExtended results (with residuals and outlier categories) saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821a7fb7-aca6-4e60-9258-8becd73655fa",
   "metadata": {},
   "source": [
    "## Outlier Analysis (based on bin based approach) -- this code was used to produce Figure 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7812a24-c144-48ea-a067-021e1adbc972",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "OUTLIER ANALYSIS: CATEGORICAL BIN APPROACH\n",
      "================================================================================\n",
      "\n",
      "This approach identifies outliers within sample size categories\n",
      "rather than using regression. Less sensitive to correlation strength.\n",
      "\n",
      "================================================================================\n",
      "SAMPLE SIZE DISTRIBUTION\n",
      "================================================================================\n",
      "\n",
      "Summary statistics for total comments:\n",
      "count        39.000000\n",
      "mean      15416.948718\n",
      "std       21276.700861\n",
      "min        1969.000000\n",
      "25%        3229.000000\n",
      "50%       11268.000000\n",
      "75%       16827.500000\n",
      "max      118368.000000\n",
      "Name: total_comments, dtype: float64\n",
      "\n",
      "Quantile-based bins (33rd, 67th percentiles):\n",
      "  Small: 1969 - 4402\n",
      "  Medium: 4402 - 14561\n",
      "  Large: 14561 - 118368\n",
      "\n",
      "Chosen bins (quantile-based): ['Small (<4402)', 'Medium (4402-14561)', 'Large (>14561)']\n",
      "\n",
      "Shows per bin:\n",
      "size_bin\n",
      "Small (<4402)          13\n",
      "Medium (4402-14561)    13\n",
      "Large (>14561)         13\n",
      "Name: count, dtype: int64\n",
      "\n",
      "================================================================================\n",
      "CALCULATING OUTLIERS WITHIN EACH BIN\n",
      "================================================================================\n",
      "\n",
      "Diversity statistics by bin:\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "Small (<4402):\n",
      "  N shows: 13\n",
      "  Mean entropy: 3.631\n",
      "  Std entropy: 0.080\n",
      "  Range: 3.493 - 3.783\n",
      "  Outliers: 1 high, 1 low\n",
      "\n",
      "Medium (4402-14561):\n",
      "  N shows: 13\n",
      "  Mean entropy: 3.672\n",
      "  Std entropy: 0.047\n",
      "  Range: 3.548 - 3.739\n",
      "  Outliers: 0 high, 1 low\n",
      "\n",
      "Large (>14561):\n",
      "  N shows: 13\n",
      "  Mean entropy: 3.686\n",
      "  Std entropy: 0.045\n",
      "  Range: 3.603 - 3.736\n",
      "  Outliers: 0 high, 1 low\n",
      "\n",
      "================================================================================\n",
      "OUTLIERS BY SIZE BIN\n",
      "================================================================================\n",
      "\n",
      "================================================================================\n",
      "BIN: Small (<4402)\n",
      "================================================================================\n",
      "\n",
      "MORE diverse than bin peers (z > 1.5):\n",
      "   SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "Lucifer            2497               3.782524       1.90254                  100.0\n",
      "\n",
      "LESS diverse than bin peers (z < -1.5):\n",
      "            SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "My Hero Academia            1969                3.49289     -1.728828               7.692308\n",
      "\n",
      "================================================================================\n",
      "BIN: Medium (4402-14561)\n",
      "================================================================================\n",
      "\n",
      "MORE diverse than bin peers (z > 1.5):\n",
      "  None\n",
      "\n",
      "LESS diverse than bin peers (z < -1.5):\n",
      "     SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "One Piece           13117               3.548109     -2.612236               7.692308\n",
      "\n",
      "================================================================================\n",
      "BIN: Large (>14561)\n",
      "================================================================================\n",
      "\n",
      "MORE diverse than bin peers (z > 1.5):\n",
      "  None\n",
      "\n",
      "LESS diverse than bin peers (z < -1.5):\n",
      "SHOW  total_comments  shannon_entropy_top15  z_within_bin  percentile_within_bin\n",
      "Dark           18834               3.603331     -1.834921               7.692308\n",
      "\n",
      "================================================================================\n",
      "COMPARISON: BIN vs REGRESSION APPROACH\n",
      "================================================================================\n",
      "\n",
      "Outliers detected (|z| > 1.5):\n",
      "  Bin approach: 4 shows\n",
      "  Regression approach: 5 shows\n",
      "  In both: 3 shows\n",
      "  Only in bin approach: 1 shows\n",
      "  Only in regression: 2 shows\n",
      "\n",
      "Shows that are outliers with BIN approach but NOT regression:\n",
      "  - Dark (Large (>14561)): z_bin=-1.83, z_reg=-1.39\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'Peak_diversity_outliers_bin_approach.png'\n",
      "Saving main outlier plot as separate image...\n",
      "Standalone bin outlier plot saved as 'diversity_outliers_bin_standalone.png'\n",
      "\n",
      "Results saved to 'Sentiment_Data/Peak_diversity_with_outliers_bin_approach.csv'\n",
      "\n",
      "================================================================================\n",
      "OUTLIER ANALYSIS COMPLETE\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Load the diversity results from previous analysis\n",
    "diversity_df = pd.read_csv('Data/Peak_emotional_diversity_results.csv')\n",
    "\n",
    "# ============================================================================\n",
    "# DEFINE SAMPLE SIZE BINS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS: CATEGORICAL BIN APPROACH\")\n",
    "print(\"=\"*80)\n",
    "print(\"\\nThis approach identifies outliers within sample size categories\")\n",
    "print(\"rather than using regression. Less sensitive to correlation strength.\")\n",
    "\n",
    "# Define bin edges - adjust these based on your data distribution\n",
    "# Let's look at the distribution first to choose sensible bins\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"SAMPLE SIZE DISTRIBUTION\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nSummary statistics for total comments:\")\n",
    "print(diversity_df['total_comments'].describe())\n",
    "\n",
    "# Define bins based on quantiles or meaningful breaks\n",
    "# Using quantile-based bins for better balance\n",
    "quantile_bins = diversity_df['total_comments'].quantile([0, 0.33, 0.67, 1.0]).tolist()\n",
    "print(f\"\\nQuantile-based bins (33rd, 67th percentiles):\")\n",
    "print(f\"  Small: {quantile_bins[0]:.0f} - {quantile_bins[1]:.0f}\")\n",
    "print(f\"  Medium: {quantile_bins[1]:.0f} - {quantile_bins[2]:.0f}\")\n",
    "print(f\"  Large: {quantile_bins[2]:.0f} - {quantile_bins[3]:.0f}\")\n",
    "\n",
    "# Use quantile bins for better balance\n",
    "bin_edges = quantile_bins\n",
    "bin_labels = [f'Small (<{quantile_bins[1]:.0f})', \n",
    "              f'Medium ({quantile_bins[1]:.0f}-{quantile_bins[2]:.0f})', \n",
    "              f'Large (>{quantile_bins[2]:.0f})']\n",
    "\n",
    "diversity_df['size_bin'] = pd.cut(diversity_df['total_comments'], \n",
    "                                   bins=bin_edges, \n",
    "                                   labels=bin_labels,\n",
    "                                   include_lowest=True)\n",
    "\n",
    "print(f\"\\nChosen bins (quantile-based): {bin_labels}\")\n",
    "print(\"\\nShows per bin:\")\n",
    "print(diversity_df['size_bin'].value_counts().sort_index())\n",
    "\n",
    "# ============================================================================\n",
    "# CALCULATE Z-SCORES WITHIN EACH BIN\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"CALCULATING OUTLIERS WITHIN EACH BIN\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Calculate z-scores within each bin\n",
    "diversity_df['z_within_bin'] = diversity_df.groupby('size_bin', observed=True)['shannon_entropy_top15'].transform(\n",
    "    lambda x: (x - x.mean()) / x.std()\n",
    ")\n",
    "\n",
    "# Also calculate percentile ranks within each bin\n",
    "diversity_df['percentile_within_bin'] = diversity_df.groupby('size_bin', observed=True)['shannon_entropy_top15'].transform(\n",
    "    lambda x: x.rank(pct=True) * 100\n",
    ")\n",
    "\n",
    "# Define outlier thresholds\n",
    "threshold_strong = 2.0  # z-score\n",
    "threshold_moderate = 1.5\n",
    "\n",
    "# Categorize outliers\n",
    "diversity_df['outlier_category_bin'] = 'Normal'\n",
    "diversity_df.loc[diversity_df['z_within_bin'] > threshold_strong, 'outlier_category_bin'] = 'Much MORE diverse'\n",
    "diversity_df.loc[(diversity_df['z_within_bin'] > threshold_moderate) & \n",
    "                 (diversity_df['z_within_bin'] <= threshold_strong), 'outlier_category_bin'] = 'More diverse'\n",
    "diversity_df.loc[diversity_df['z_within_bin'] < -threshold_strong, 'outlier_category_bin'] = 'Much LESS diverse'\n",
    "diversity_df.loc[(diversity_df['z_within_bin'] < -threshold_moderate) & \n",
    "                 (diversity_df['z_within_bin'] >= -threshold_strong), 'outlier_category_bin'] = 'Less diverse'\n",
    "\n",
    "# ============================================================================\n",
    "# STATISTICS BY BIN\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\nDiversity statistics by bin:\")\n",
    "print(\"\\n\" + \"-\"*80)\n",
    "\n",
    "for bin_label in bin_labels:\n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label]\n",
    "    if len(bin_data) > 0:\n",
    "        print(f\"\\n{bin_label}:\")\n",
    "        print(f\"  N shows: {len(bin_data)}\")\n",
    "        print(f\"  Mean entropy: {bin_data['shannon_entropy_top15'].mean():.3f}\")\n",
    "        print(f\"  Std entropy: {bin_data['shannon_entropy_top15'].std():.3f}\")\n",
    "        print(f\"  Range: {bin_data['shannon_entropy_top15'].min():.3f} - {bin_data['shannon_entropy_top15'].max():.3f}\")\n",
    "        \n",
    "        # Count outliers in this bin\n",
    "        n_high = (bin_data['z_within_bin'] > threshold_moderate).sum()\n",
    "        n_low = (bin_data['z_within_bin'] < -threshold_moderate).sum()\n",
    "        print(f\"  Outliers: {n_high} high, {n_low} low\")\n",
    "\n",
    "# ============================================================================\n",
    "# DISPLAY OUTLIERS BY BIN\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIERS BY SIZE BIN\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "for bin_label in bin_labels:\n",
    "    print(\"\\n\" + \"=\"*80)\n",
    "    print(f\"BIN: {bin_label}\")\n",
    "    print(\"=\"*80)\n",
    "    \n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label].copy()\n",
    "    \n",
    "    if len(bin_data) == 0:\n",
    "        print(\"No shows in this bin\")\n",
    "        continue\n",
    "    \n",
    "    # High outliers\n",
    "    high_outliers = bin_data[bin_data['z_within_bin'] > threshold_moderate].sort_values(\n",
    "        'z_within_bin', ascending=False\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nMORE diverse than bin peers (z > 1.5):\")\n",
    "    if len(high_outliers) > 0:\n",
    "        print(high_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                             'z_within_bin', 'percentile_within_bin']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"  None\")\n",
    "    \n",
    "    # Low outliers\n",
    "    low_outliers = bin_data[bin_data['z_within_bin'] < -threshold_moderate].sort_values(\n",
    "        'z_within_bin'\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nLESS diverse than bin peers (z < -1.5):\")\n",
    "    if len(low_outliers) > 0:\n",
    "        print(low_outliers[['SHOW', 'total_comments', 'shannon_entropy_top15', \n",
    "                            'z_within_bin', 'percentile_within_bin']].to_string(index=False))\n",
    "    else:\n",
    "        print(\"  None\")\n",
    "\n",
    "# ============================================================================\n",
    "# COMPARE TO REGRESSION APPROACH\n",
    "# ============================================================================\n",
    "\n",
    "# Load or calculate regression-based outliers for comparison\n",
    "diversity_df['log_comments'] = np.log10(diversity_df['total_comments'])\n",
    "slope, intercept, r_value, p_value, se = stats.linregress(\n",
    "    diversity_df['log_comments'], \n",
    "    diversity_df['shannon_entropy_top15']\n",
    ")\n",
    "diversity_df['predicted_diversity'] = slope * diversity_df['log_comments'] + intercept\n",
    "diversity_df['residual'] = diversity_df['shannon_entropy_top15'] - diversity_df['predicted_diversity']\n",
    "diversity_df['z_regression'] = (diversity_df['residual'] - diversity_df['residual'].mean()) / diversity_df['residual'].std()\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPARISON: BIN vs REGRESSION APPROACH\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Shows that are outliers in one but not the other\n",
    "bin_outliers = set(diversity_df[abs(diversity_df['z_within_bin']) > threshold_moderate]['SHOW'])\n",
    "reg_outliers = set(diversity_df[abs(diversity_df['z_regression']) > threshold_moderate]['SHOW'])\n",
    "\n",
    "only_bin = bin_outliers - reg_outliers\n",
    "only_reg = reg_outliers - bin_outliers\n",
    "both = bin_outliers & reg_outliers\n",
    "\n",
    "print(f\"\\nOutliers detected (|z| > 1.5):\")\n",
    "print(f\"  Bin approach: {len(bin_outliers)} shows\")\n",
    "print(f\"  Regression approach: {len(reg_outliers)} shows\")\n",
    "print(f\"  In both: {len(both)} shows\")\n",
    "print(f\"  Only in bin approach: {len(only_bin)} shows\")\n",
    "print(f\"  Only in regression: {len(only_reg)} shows\")\n",
    "\n",
    "if len(only_bin) > 0:\n",
    "    print(f\"\\nShows that are outliers with BIN approach but NOT regression:\")\n",
    "    for show in list(only_bin)[:10]:  # Show first 10\n",
    "        z_bin = diversity_df.loc[diversity_df['SHOW'] == show, 'z_within_bin'].values[0]\n",
    "        z_reg = diversity_df.loc[diversity_df['SHOW'] == show, 'z_regression'].values[0]\n",
    "        bin_label = diversity_df.loc[diversity_df['SHOW'] == show, 'size_bin'].values[0]\n",
    "        print(f\"  - {show} ({bin_label}): z_bin={z_bin:.2f}, z_reg={z_reg:.2f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12), constrained_layout=True)\n",
    "\n",
    "# Plot 1: Diversity by bin (box plot)\n",
    "ax1 = axes[0, 0]\n",
    "bin_order = bin_labels\n",
    "sns.boxplot(data=diversity_df, x='size_bin', y='shannon_entropy_top15', \n",
    "            order=bin_order, ax=ax1)\n",
    "\n",
    "# Add individual points\n",
    "sns.stripplot(data=diversity_df, x='size_bin', y='shannon_entropy_top15',\n",
    "              order=bin_order, ax=ax1, color='black', alpha=0.3, size=4)\n",
    "\n",
    "# Highlight outliers\n",
    "outlier_data = diversity_df[abs(diversity_df['z_within_bin']) > threshold_moderate]\n",
    "if len(outlier_data) > 0:\n",
    "    # Map bin labels to positions\n",
    "    bin_positions = {label: i for i, label in enumerate(bin_order)}\n",
    "    x_positions = [bin_positions[label] for label in outlier_data['size_bin']]\n",
    "    ax1.scatter(x=x_positions,\n",
    "               y=outlier_data['shannon_entropy_top15'],\n",
    "               s=100, facecolors='none', edgecolors='red', linewidths=2,\n",
    "               label='Outliers', zorder=5)\n",
    "\n",
    "ax1.set_xlabel('Sample Size Bin')\n",
    "ax1.set_ylabel('Shannon Entropy (Top 15 Emotions)')\n",
    "ax1.set_title('Diversity Distribution by Sample Size Bin\\nRed circles = outliers within bin')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Plot 2: Z-scores within bin\n",
    "ax2 = axes[0, 1]\n",
    "colors_map = {'Normal': 'gray', 'More diverse': 'lightblue', 'Much MORE diverse': 'darkblue',\n",
    "              'Less diverse': 'lightcoral', 'Much LESS diverse': 'darkred'}\n",
    "\n",
    "for category in diversity_df['outlier_category_bin'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category_bin'] == category]\n",
    "    ax2.scatter(subset['total_comments'], subset['z_within_bin'], \n",
    "               label=category, alpha=0.7, s=60, color=colors_map.get(category, 'gray'))\n",
    "\n",
    "ax2.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.5)\n",
    "ax2.axhline(threshold_moderate, color='blue', linestyle=':', alpha=0.5)\n",
    "ax2.axhline(-threshold_moderate, color='red', linestyle=':', alpha=0.5)\n",
    "\n",
    "# Add vertical lines for bin boundaries\n",
    "for edge in bin_edges[1:-1]:  # Skip first and last\n",
    "    ax2.axvline(edge, color='gray', linestyle='--', alpha=0.3, linewidth=1)\n",
    "\n",
    "ax2.set_xlabel('Total Comments (log scale)')\n",
    "ax2.set_ylabel('Z-score Within Bin')\n",
    "ax2.set_title('Z-Scores Within Sample Size Bins\\n(Outliers relative to bin peers)')\n",
    "ax2.set_xscale('log')\n",
    "ax2.legend(loc='best', fontsize=8)\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Add bin labels at top\n",
    "for i, (edge_start, edge_end, label) in enumerate(zip(bin_edges[:-1], bin_edges[1:], bin_labels)):\n",
    "    mid = np.sqrt(edge_start * edge_end) if edge_end != float('inf') else edge_start * 3\n",
    "    ax2.text(mid, ax2.get_ylim()[1] * 0.9, label, \n",
    "            ha='center', fontsize=9, style='italic', alpha=0.7)\n",
    "\n",
    "# Label outliers\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    if row['outlier_category_bin'] != 'Normal':\n",
    "        ax2.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=7, alpha=0.8)\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax2.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=8, alpha=0.9, fontweight='bold')\n",
    "\n",
    "# Plot 3: Percentile ranks within bin\n",
    "ax3 = axes[0, 2]\n",
    "for bin_label in bin_labels:\n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label]\n",
    "    ax3.hist(bin_data['percentile_within_bin'], bins=20, alpha=0.5, \n",
    "            label=bin_label, edgecolor='black')\n",
    "\n",
    "ax3.axvline(50, color='black', linestyle='--', linewidth=2, label='Median')\n",
    "ax3.set_xlabel('Percentile Rank Within Bin')\n",
    "ax3.set_ylabel('Number of Shows')\n",
    "ax3.set_title('Distribution of Within-Bin Percentile Ranks')\n",
    "ax3.legend()\n",
    "ax3.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Plot 4: Comparison with regression approach\n",
    "ax4 = axes[1, 0]\n",
    "scatter = ax4.scatter(diversity_df['z_regression'], diversity_df['z_within_bin'], \n",
    "           alpha=0.6, s=50, c=diversity_df['total_comments'], \n",
    "           cmap='viridis', norm=plt.matplotlib.colors.LogNorm())\n",
    "ax4.plot([-3, 3], [-3, 3], 'k--', alpha=0.3, label='y=x')\n",
    "ax4.axhline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax4.axvline(0, color='gray', linestyle=':', alpha=0.5)\n",
    "ax4.set_xlabel('Z-score (Regression Approach)')\n",
    "ax4.set_ylabel('Z-score (Bin Approach)')\n",
    "ax4.set_title(f'Comparison: Bin vs Regression\\n(color = sample size, r={r_value:.3f})')\n",
    "ax4.legend()\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Fix colorbar creation\n",
    "try:\n",
    "    cbar = plt.colorbar(scatter, ax=ax4)\n",
    "    cbar.set_label('Total Comments')\n",
    "except Exception as e:\n",
    "    print(f\"Warning: Could not create colorbar for plot 4: {e}\")\n",
    "\n",
    "# Annotate shows that differ significantly between methods\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    z_diff = abs(row['z_regression'] - row['z_within_bin'])\n",
    "    if z_diff > 1.5:\n",
    "        ax4.annotate(row['SHOW'], \n",
    "                    (row['z_regression'], row['z_within_bin']),\n",
    "                    fontsize=7, alpha=0.7)\n",
    "\n",
    "# Plot 5: Mean diversity by bin with error bars\n",
    "ax5 = axes[1, 1]\n",
    "bin_stats = diversity_df.groupby('size_bin', observed=True)['shannon_entropy_top15'].agg(['mean', 'std', 'count'])\n",
    "bin_stats = bin_stats.reindex(bin_labels)\n",
    "\n",
    "x_pos = range(len(bin_labels))\n",
    "ax5.bar(x_pos, bin_stats['mean'], yerr=bin_stats['std'], \n",
    "       capsize=5, alpha=0.7, edgecolor='black', color='steelblue')\n",
    "ax5.set_xticks(x_pos)\n",
    "ax5.set_xticklabels(bin_labels, rotation=45, ha='right')\n",
    "ax5.set_ylabel('Mean Shannon Entropy (Top 15)')\n",
    "ax5.set_title('Mean Diversity by Sample Size Bin\\n(Error bars = ±1 SD)')\n",
    "ax5.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Add N labels\n",
    "for i, (idx, row) in enumerate(bin_stats.iterrows()):\n",
    "    ax5.text(i, row['mean'] + row['std'] + 0.05, f\"n={int(row['count'])}\", \n",
    "            ha='center', fontsize=9)\n",
    "\n",
    "# Plot 6: Outlier counts by bin (simplified to avoid rendering issues)\n",
    "ax6 = axes[1, 2]\n",
    "\n",
    "# Count outliers per bin per category\n",
    "outlier_summary = []\n",
    "for bin_label in bin_labels:\n",
    "    bin_data = diversity_df[diversity_df['size_bin'] == bin_label]\n",
    "    for category in ['Much MORE diverse', 'More diverse', 'Normal', 'Less diverse', 'Much LESS diverse']:\n",
    "        count = (bin_data['outlier_category_bin'] == category).sum()\n",
    "        outlier_summary.append({\n",
    "            'bin': bin_label,\n",
    "            'category': category,\n",
    "            'count': count\n",
    "        })\n",
    "\n",
    "outlier_summary_df = pd.DataFrame(outlier_summary)\n",
    "\n",
    "# Create grouped bar chart instead of stacked\n",
    "categories_to_plot = ['Much MORE diverse', 'More diverse', 'Less diverse', 'Much LESS diverse']\n",
    "x_pos = np.arange(len(bin_labels))\n",
    "width = 0.2\n",
    "\n",
    "for i, category in enumerate(categories_to_plot):\n",
    "    counts = [outlier_summary_df[(outlier_summary_df['bin'] == bin_label) & \n",
    "                                  (outlier_summary_df['category'] == category)]['count'].values[0]\n",
    "              for bin_label in bin_labels]\n",
    "    ax6.bar(x_pos + i * width, counts, width, \n",
    "           label=category, color=colors_map.get(category, 'gray'), alpha=0.8)\n",
    "\n",
    "ax6.set_xlabel('Sample Size Bin')\n",
    "ax6.set_ylabel('Number of Shows')\n",
    "ax6.set_title('Outlier Counts by Bin')\n",
    "ax6.set_xticks(x_pos + width * 1.5)\n",
    "ax6.set_xticklabels(bin_labels, rotation=15, ha='right', fontsize=8)\n",
    "ax6.legend(fontsize=7, loc='upper left')\n",
    "ax6.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "plt.savefig('Figures/Peak_diversity_outliers_bin_approach.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'Peak_diversity_outliers_bin_approach.png'\")\n",
    "\n",
    "# Save standalone version of plot 2 (main outlier plot)\n",
    "print(\"Saving main outlier plot as separate image...\")\n",
    "fig_standalone = plt.figure(figsize=(10, 6))\n",
    "ax_standalone = fig_standalone.add_subplot(111)\n",
    "\n",
    "for category in diversity_df['outlier_category_bin'].unique():\n",
    "    subset = diversity_df[diversity_df['outlier_category_bin'] == category]\n",
    "    ax_standalone.scatter(subset['total_comments'], subset['z_within_bin'], \n",
    "               label=category, alpha=0.7, s=80, color=colors_map.get(category, 'gray'))\n",
    "\n",
    "ax_standalone.axhline(0, color='black', linestyle='--', linewidth=1.5, alpha=0.5)\n",
    "ax_standalone.axhline(threshold_moderate, color='blue', linestyle=':', linewidth=1.5, alpha=0.5)\n",
    "ax_standalone.axhline(-threshold_moderate, color='red', linestyle=':', linewidth=1.5, alpha=0.5)\n",
    "\n",
    "# Add vertical lines for bin boundaries\n",
    "for edge in bin_edges[1:-1]:\n",
    "    ax_standalone.axvline(edge, color='gray', linestyle='--', alpha=0.3, linewidth=2)\n",
    "\n",
    "ax_standalone.set_xlabel('Total Comments (log scale)', fontsize=13)\n",
    "ax_standalone.set_ylabel('Z-score Within Bin', fontsize=13)\n",
    "ax_standalone.set_title('Emotional Diversity Outliers Within Sample Size Bins\\n(Top 15 Emotions)', fontsize=15)\n",
    "ax_standalone.set_xscale('log')\n",
    "ax_standalone.legend(loc='best', fontsize=11)\n",
    "ax_standalone.grid(True, alpha=0.3)\n",
    "\n",
    "# Add bin labels\n",
    "#for i, (edge_start, edge_end, label) in enumerate(zip(bin_edges[:-1], bin_edges[1:], bin_labels)):\n",
    "#    mid = np.sqrt(edge_start * edge_end) if edge_end != float('inf') else edge_start * 3\n",
    "#    ax_standalone.text(mid, ax_standalone.get_ylim()[1] * 0.92, label, \n",
    "#            ha='center', fontsize=11, style='italic', alpha=0.7,\n",
    "#            bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.3))\n",
    "\n",
    "# Label outliers and Squid Game\n",
    "for idx, row in diversity_df.iterrows():\n",
    "    if row['outlier_category_bin'] != 'Normal':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=9, alpha=0.85)\n",
    "    elif row['SHOW'] == 'Squid Game':\n",
    "        ax_standalone.annotate(row['SHOW'], \n",
    "                    xy=(row['total_comments'], row['z_within_bin']),\n",
    "                    xytext=(5, 5), textcoords='offset points',\n",
    "                    fontsize=10, alpha=0.95, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Figures/Figure17.png', dpi=300, bbox_inches='tight')\n",
    "print(\"Standalone bin outlier plot saved as 'diversity_outliers_bin_standalone.png'\")\n",
    "plt.close(fig_standalone)\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "output_file = 'Sentiment_Data/Peak_diversity_with_outliers_bin_approach.csv'\n",
    "diversity_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nResults saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"OUTLIER ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88ba6be5-64c8-4a47-a6d1-a1f78394394d",
   "metadata": {},
   "source": [
    "### Simple Analytics on Peak Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ae66693-9db2-48be-8071-ed668f05241d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "SQUID GAME COMPARATIVE ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "Analyzing 39 shows across 15 emotions\n",
      "\n",
      "================================================================================\n",
      "1. Z-SCORE ANALYSIS: How different is Squid Game?\n",
      "================================================================================\n",
      "\n",
      "Squid Game's most distinctive emotions (by z-score):\n",
      "\n",
      "Highest (more than other shows):\n",
      "Disapproval    0.152400\n",
      "Admiration     0.269955\n",
      "Realization    0.991517\n",
      "Approval       1.284315\n",
      "Sadness        1.714647\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "Lowest (less than other shows):\n",
      "Amusement   -0.550306\n",
      "Joy         -0.489610\n",
      "Optimism    -0.402443\n",
      "Desire      -0.364763\n",
      "Confusion   -0.275402\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "================================================================================\n",
      "2. MOST/LEAST SIMILAR SHOWS TO SQUID GAME\n",
      "================================================================================\n",
      "\n",
      "Top 10 most similar shows (cosine similarity - pattern-based):\n",
      "   1. Better Call Saul               (similarity: 0.9797)\n",
      "   2. Stranger Things                (similarity: 0.9767)\n",
      "   3. The Walking Dead               (similarity: 0.9696)\n",
      "   4. The Umbrella Academy           (similarity: 0.9657)\n",
      "   5. The 100                        (similarity: 0.9616)\n",
      "   6. The Handmaid's Tale            (similarity: 0.9614)\n",
      "   7. Attack On Titan                (similarity: 0.9609)\n",
      "   8. Peaky Blinders                 (similarity: 0.9570)\n",
      "   9. Lucifer                        (similarity: 0.9567)\n",
      "  10. The Wheel Of Time              (similarity: 0.9543)\n",
      "\n",
      "Top 10 least similar shows (cosine similarity - pattern-based):\n",
      "   1. Dark                           (similarity: 0.8686)\n",
      "   2. My Hero Academia               (similarity: 0.8841)\n",
      "   3. Vikings                        (similarity: 0.8844)\n",
      "   4. Hawkeye                        (similarity: 0.8904)\n",
      "   5. One Piece                      (similarity: 0.8942)\n",
      "   6. Dragon Ball Super              (similarity: 0.8975)\n",
      "   7. La Casa De Papel               (similarity: 0.9006)\n",
      "   8. Moon Knight                    (similarity: 0.9055)\n",
      "   9. The Flash                      (similarity: 0.9061)\n",
      "  10. The Mandalorian                (similarity: 0.9080)\n",
      "\n",
      "Top 10 closest shows (Euclidean distance - magnitude-based):\n",
      "   1. Stranger Things                (distance: 2.3457)\n",
      "   2. The Walking Dead               (distance: 2.6744)\n",
      "   3. The Umbrella Academy           (distance: 2.8793)\n",
      "   4. The 100                        (distance: 3.0377)\n",
      "   5. Better Call Saul               (distance: 3.0958)\n",
      "   6. The Handmaid's Tale            (distance: 3.1894)\n",
      "   7. Peaky Blinders                 (distance: 3.2446)\n",
      "   8. Lucifer                        (distance: 3.2743)\n",
      "   9. Attack On Titan                (distance: 3.3406)\n",
      "  10. The Falcon And The Winter Soldier (distance: 3.3547)\n",
      "\n",
      "================================================================================\n",
      "3. PERCENTILE RANKINGS: Where does Squid Game rank on each emotion?\n",
      "================================================================================\n",
      "\n",
      "Squid Game's percentile rankings:\n",
      "\n",
      "Highest percentiles (top of distribution):\n",
      "Sadness        97.435897\n",
      "Approval       94.871795\n",
      "Realization    87.179487\n",
      "Admiration     69.230769\n",
      "Anger          66.666667\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "Lowest percentiles (bottom of distribution):\n",
      "Optimism     43.589744\n",
      "Curiosity    43.589744\n",
      "Confusion    41.025641\n",
      "Amusement    38.461538\n",
      "Joy          35.897436\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "================================================================================\n",
      "4. WHICH EMOTIONS MAKE SQUID GAME MOST DISTINCTIVE?\n",
      "================================================================================\n",
      "\n",
      "Contribution to overall distinctiveness (squared z-scores):\n",
      "Sadness           2.940015\n",
      "Approval          1.649465\n",
      "Realization       0.983107\n",
      "Amusement         0.302836\n",
      "Joy               0.239718\n",
      "Optimism          0.161961\n",
      "Desire            0.133052\n",
      "Confusion         0.075846\n",
      "Admiration        0.072875\n",
      "Love              0.034087\n",
      "Disapproval       0.023226\n",
      "Annoyance         0.022873\n",
      "Disappointment    0.014316\n",
      "Anger             0.000651\n",
      "Curiosity         0.000103\n",
      "Name: Squid Game, dtype: float64\n",
      "\n",
      "Total distinctiveness: 6.65\n",
      "Top 3 emotions account for 83.7% of distinctiveness\n",
      "\n",
      "================================================================================\n",
      "5. PCA ANALYSIS: Position in emotion space\n",
      "================================================================================\n",
      "\n",
      "PC1 explains 32.7% of variance\n",
      "PC2 explains 15.1% of variance\n",
      "Combined: 47.8% of variance\n",
      "\n",
      "Squid Game position: PC1=0.547, PC2=-0.236\n",
      "\n",
      "================================================================================\n",
      "GENERATING VISUALIZATIONS...\n",
      "================================================================================\n",
      "\n",
      "Visualization saved as 'squid_game_comparative_analysis_peak.png'\n",
      "\n",
      "Detailed comparison data saved to 'Sentiment_Data/squid_game_show_comparisons_peak.csv'\n",
      "\n",
      "================================================================================\n",
      "ANALYSIS COMPLETE\n",
      "================================================================================\n",
      "\n",
      "================================================================================\n",
      "KEY INSIGHTS SUMMARY\n",
      "================================================================================\n",
      "\n",
      "1. MOST SIMILAR SHOWS:\n",
      "   - By pattern (cosine): Better Call Saul\n",
      "   - By magnitude (Euclidean): Stranger Things\n",
      "\n",
      "2. MOST DISTINCTIVE EMOTIONS:\n",
      "   - Primary: Sadness (contributes 44.2% to distinctiveness)\n",
      "\n",
      "3. EXTREME PERCENTILES:\n",
      "   - Highest: Sadness (97.4th percentile)\n",
      "   - Lowest: Joy (35.9th percentile)\n",
      "\n",
      "4. POSITION IN EMOTION SPACE:\n",
      "   - PC1: 0.547 (explains 32.7% of variance)\n",
      "   - PC2: -0.236 (explains 15.1% of variance)\n",
      "\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import zscore\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n",
    "from math import pi\n",
    "\n",
    "# Load data\n",
    "df = pd.read_csv('Data/Final_InSentiment_ByShow_Peak.csv')\n",
    "\n",
    "# Define top 15 emotions\n",
    "top15_emotions = [\n",
    "    \"[0, 'Admiration']\", \"[4, 'Approval']\", \"[18, 'Love']\", \n",
    "    \"[10, 'Disapproval']\", \"[3, 'Annoyance']\", \"[6, 'Confusion']\",\n",
    "    \"[1, 'Amusement']\", \"[25, 'Sadness']\", \"[9, 'Disappointment']\", \n",
    "    \"[22, 'Realization']\", \"[7, 'Curiosity']\", \"[2, 'Anger']\",\n",
    "    \"[17, 'Joy']\", \"[20, 'Optimism']\", \"[8, 'Desire']\"\n",
    "]\n",
    "\n",
    "# Filter to top 15 emotions (excluding Neutral for this analysis)\n",
    "filtered_df = df[df['PREDICTION'].isin(top15_emotions)]\n",
    "\n",
    "# Pivot to get shows x emotions matrix\n",
    "pivot_df = filtered_df.pivot_table(\n",
    "    index='SHOW', \n",
    "    columns='PREDICTION', \n",
    "    values='PERCENTAGES',\n",
    "    fill_value=0\n",
    ")\n",
    "\n",
    "# Clean up column names for readability\n",
    "pivot_df.columns = [col.split(\"'\")[1] for col in pivot_df.columns]\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"SQUID GAME COMPARATIVE ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "print(f\"\\nAnalyzing {len(pivot_df)} shows across {len(pivot_df.columns)} emotions\")\n",
    "\n",
    "# ============================================================================\n",
    "# 1. Z-SCORE ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"1. Z-SCORE ANALYSIS: How different is Squid Game?\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "z_df = pivot_df.apply(zscore)\n",
    "sg_z = z_df.loc['Squid Game'].sort_values()\n",
    "\n",
    "print(\"\\nSquid Game's most distinctive emotions (by z-score):\")\n",
    "print(\"\\nHighest (more than other shows):\")\n",
    "print(sg_z.tail(5))\n",
    "print(\"\\nLowest (less than other shows):\")\n",
    "print(sg_z.head(5))\n",
    "\n",
    "# ============================================================================\n",
    "# 2. NEAREST NEIGHBORS ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"2. MOST/LEAST SIMILAR SHOWS TO SQUID GAME\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Cosine similarity (pattern-based)\n",
    "similarities = cosine_similarity(pivot_df)\n",
    "sim_df = pd.DataFrame(similarities, index=pivot_df.index, columns=pivot_df.index)\n",
    "squid_game_similar_cosine = sim_df['Squid Game'].sort_values(ascending=False)[1:11]\n",
    "\n",
    "print(\"\\nTop 10 most similar shows (cosine similarity - pattern-based):\")\n",
    "for i, (show, sim) in enumerate(squid_game_similar_cosine.items(), 1):\n",
    "    print(f\"  {i:2d}. {show:<30s} (similarity: {sim:.4f})\")\n",
    "\n",
    "squid_game_similar_cosine = sim_df['Squid Game'].sort_values(ascending=True)[1:11]\n",
    "print(\"\\nTop 10 least similar shows (cosine similarity - pattern-based):\")\n",
    "for i, (show, sim) in enumerate(squid_game_similar_cosine.items(), 1):\n",
    "    print(f\"  {i:2d}. {show:<30s} (similarity: {sim:.4f})\")\n",
    "\n",
    "#revert back\n",
    "squid_game_similar_cosine = sim_df['Squid Game'].sort_values(ascending=False)[1:11]\n",
    "\n",
    "# Euclidean distance (magnitude-based)\n",
    "distances = euclidean_distances(pivot_df)\n",
    "dist_df = pd.DataFrame(distances, index=pivot_df.index, columns=pivot_df.index)\n",
    "squid_game_closest_euclidean = dist_df['Squid Game'].sort_values()[1:11]\n",
    "\n",
    "print(\"\\nTop 10 closest shows (Euclidean distance - magnitude-based):\")\n",
    "for i, (show, dist) in enumerate(squid_game_closest_euclidean.items(), 1):\n",
    "    print(f\"  {i:2d}. {show:<30s} (distance: {dist:.4f})\")\n",
    "\n",
    "# ============================================================================\n",
    "# 3. PERCENTILE RANKING\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"3. PERCENTILE RANKINGS: Where does Squid Game rank on each emotion?\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "percentile_ranks = pivot_df.rank(pct=True).loc['Squid Game'] * 100\n",
    "percentile_ranks_sorted = percentile_ranks.sort_values(ascending=False)\n",
    "\n",
    "print(\"\\nSquid Game's percentile rankings:\")\n",
    "print(\"\\nHighest percentiles (top of distribution):\")\n",
    "print(percentile_ranks_sorted.head(5))\n",
    "print(\"\\nLowest percentiles (bottom of distribution):\")\n",
    "print(percentile_ranks_sorted.tail(5))\n",
    "\n",
    "# ============================================================================\n",
    "# 4. DISTINCTIVENESS CONTRIBUTION\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"4. WHICH EMOTIONS MAKE SQUID GAME MOST DISTINCTIVE?\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Squared z-scores show contribution to overall distance\n",
    "sg_distinctiveness = (z_df.loc['Squid Game'] ** 2).sort_values(ascending=False)\n",
    "\n",
    "print(\"\\nContribution to overall distinctiveness (squared z-scores):\")\n",
    "print(sg_distinctiveness)\n",
    "\n",
    "total_distinctiveness = sg_distinctiveness.sum()\n",
    "print(f\"\\nTotal distinctiveness: {total_distinctiveness:.2f}\")\n",
    "print(f\"Top 3 emotions account for {sg_distinctiveness.head(3).sum()/total_distinctiveness*100:.1f}% of distinctiveness\")\n",
    "\n",
    "# ============================================================================\n",
    "# 5. PCA ANALYSIS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"5. PCA ANALYSIS: Position in emotion space\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(pivot_df)\n",
    "\n",
    "# PCA to 2 components\n",
    "pca = PCA(n_components=2)\n",
    "pca_coords = pca.fit_transform(scaled_data)\n",
    "\n",
    "print(f\"\\nPC1 explains {pca.explained_variance_ratio_[0]*100:.1f}% of variance\")\n",
    "print(f\"PC2 explains {pca.explained_variance_ratio_[1]*100:.1f}% of variance\")\n",
    "print(f\"Combined: {sum(pca.explained_variance_ratio_)*100:.1f}% of variance\")\n",
    "\n",
    "# Get Squid Game's position\n",
    "sg_idx = list(pivot_df.index).index('Squid Game')\n",
    "sg_pc1 = pca_coords[sg_idx, 0]\n",
    "sg_pc2 = pca_coords[sg_idx, 1]\n",
    "\n",
    "print(f\"\\nSquid Game position: PC1={sg_pc1:.3f}, PC2={sg_pc2:.3f}\")\n",
    "\n",
    "# ============================================================================\n",
    "# VISUALIZATIONS\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"GENERATING VISUALIZATIONS...\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 12))\n",
    "\n",
    "# Plot 1: Z-scores\n",
    "ax1 = plt.subplot(2, 3, 1)\n",
    "colors = ['red' if x > 0 else 'blue' for x in sg_z.values]\n",
    "ax1.barh(range(len(sg_z)), sg_z.values, color=colors, alpha=0.7)\n",
    "ax1.set_yticks(range(len(sg_z)))\n",
    "ax1.set_yticklabels(sg_z.index)\n",
    "ax1.axvline(0, color='black', linestyle='--', linewidth=1)\n",
    "ax1.set_xlabel('Z-score')\n",
    "ax1.set_title('Squid Game Z-Scores vs All Shows\\n(Red=Above avg, Blue=Below avg)')\n",
    "ax1.grid(True, alpha=0.3, axis='x')\n",
    "\n",
    "# Plot 2: Percentile rankings\n",
    "ax2 = plt.subplot(2, 3, 2)\n",
    "colors = ['red' if x > 50 else 'blue' for x in percentile_ranks_sorted.values]\n",
    "ax2.barh(range(len(percentile_ranks_sorted)), percentile_ranks_sorted.values, color=colors, alpha=0.7)\n",
    "ax2.set_yticks(range(len(percentile_ranks_sorted)))\n",
    "ax2.set_yticklabels(percentile_ranks_sorted.index)\n",
    "ax2.axvline(50, color='black', linestyle='--', linewidth=1, label='Median')\n",
    "ax2.set_xlabel('Percentile Rank')\n",
    "ax2.set_title('Squid Game Percentile Rankings\\n(50 = median show)')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3, axis='x')\n",
    "\n",
    "# Plot 3: Distinctiveness contribution\n",
    "ax3 = plt.subplot(2, 3, 3)\n",
    "top_distinctive = sg_distinctiveness.head(8)\n",
    "ax3.pie(top_distinctive.values, labels=top_distinctive.index, autopct='%1.1f%%',\n",
    "        startangle=90)\n",
    "ax3.set_title('Top 8 Emotions Contributing to\\nSquid Game\\'s Distinctiveness')\n",
    "\n",
    "# Plot 4: PCA scatter\n",
    "ax4 = plt.subplot(2, 3, 4)\n",
    "ax4.scatter(pca_coords[:, 0], pca_coords[:, 1], alpha=0.5, s=50)\n",
    "\n",
    "# Highlight Squid Game\n",
    "ax4.scatter(pca_coords[sg_idx, 0], pca_coords[sg_idx, 1], \n",
    "           color='red', s=300, marker='*', label='Squid Game', zorder=5)\n",
    "\n",
    "# Annotate Squid Game\n",
    "ax4.annotate('Squid Game', \n",
    "            xy=(pca_coords[sg_idx, 0], pca_coords[sg_idx, 1]),\n",
    "            xytext=(10, 10), textcoords='offset points',\n",
    "            fontsize=10, fontweight='bold', color='red')\n",
    "\n",
    "# Annotate nearest neighbors\n",
    "threshold = np.percentile(dist_df.loc['Squid Game'], 10)  # Top 10% closest\n",
    "for i, show in enumerate(pivot_df.index):\n",
    "    if show != 'Squid Game' and dist_df.loc['Squid Game', show] < threshold:\n",
    "        ax4.annotate(show, \n",
    "                    (pca_coords[i, 0], pca_coords[i, 1]),\n",
    "                    fontsize=8, alpha=0.7)\n",
    "        # Draw line to Squid Game\n",
    "        ax4.plot([pca_coords[sg_idx, 0], pca_coords[i, 0]], \n",
    "                [pca_coords[sg_idx, 1], pca_coords[i, 1]], \n",
    "                'r--', alpha=0.3, linewidth=0.5)\n",
    "\n",
    "ax4.set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}% variance)')\n",
    "ax4.set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}% variance)')\n",
    "ax4.set_title('Shows in Emotion Space (PCA)\\nLines show closest neighbors')\n",
    "ax4.legend()\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 5: Radar plot comparison\n",
    "ax5 = plt.subplot(2, 3, 5, projection='polar')\n",
    "\n",
    "# Get profiles to compare\n",
    "mean_profile = pivot_df.mean()\n",
    "sg_profile = pivot_df.loc['Squid Game']\n",
    "most_similar_show = squid_game_similar_cosine.index[0]\n",
    "similar_profile = pivot_df.loc[most_similar_show]\n",
    "\n",
    "categories = list(pivot_df.columns)\n",
    "N = len(categories)\n",
    "angles = [n / float(N) * 2 * pi for n in range(N)]\n",
    "angles += angles[:1]\n",
    "\n",
    "# Plot each profile\n",
    "for name, profile, color in [\n",
    "    ('Mean', mean_profile, 'gray'),\n",
    "    ('Squid Game', sg_profile, 'red'),\n",
    "    (f'{most_similar_show}', similar_profile, 'blue')\n",
    "]:\n",
    "    values = profile.tolist()\n",
    "    values += values[:1]\n",
    "    ax5.plot(angles, values, label=name, linewidth=2, color=color)\n",
    "    ax5.fill(angles, values, alpha=0.1, color=color)\n",
    "\n",
    "ax5.set_xticks(angles[:-1])\n",
    "ax5.set_xticklabels(categories, size=8)\n",
    "ax5.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))\n",
    "ax5.set_title('Emotion Profile Comparison\\n(Squid Game vs Mean vs Most Similar)')\n",
    "\n",
    "# Plot 6: Distance distribution\n",
    "ax6 = plt.subplot(2, 3, 6)\n",
    "all_distances = dist_df.loc['Squid Game'].values\n",
    "sg_distance_percentile = (all_distances < 0).sum() / len(all_distances) * 100\n",
    "\n",
    "ax6.hist(all_distances, bins=30, edgecolor='black', alpha=0.7, color='steelblue')\n",
    "ax6.axvline(0, color='red', linestyle='--', linewidth=2, \n",
    "           label='Squid Game\\n(reference point)')\n",
    "ax6.set_xlabel('Euclidean Distance from Squid Game')\n",
    "ax6.set_ylabel('Number of Shows')\n",
    "ax6.set_title('Distribution of Shows by Distance from Squid Game')\n",
    "ax6.legend()\n",
    "ax6.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Figures/squid_game_comparative_analysis_peak.png', dpi=300, bbox_inches='tight')\n",
    "print(\"\\nVisualization saved as 'squid_game_comparative_analysis_peak.png'\")\n",
    "\n",
    "# ============================================================================\n",
    "# SAVE DETAILED RESULTS\n",
    "# ============================================================================\n",
    "\n",
    "# Create summary dataframe\n",
    "summary_df = pd.DataFrame({\n",
    "    'show': pivot_df.index,\n",
    "    'euclidean_distance': dist_df.loc['Squid Game'],\n",
    "    'cosine_similarity': sim_df.loc['Squid Game'],\n",
    "    'pc1': pca_coords[:, 0],\n",
    "    'pc2': pca_coords[:, 1]\n",
    "}).sort_values('euclidean_distance')\n",
    "\n",
    "output_file = 'Data/squid_game_show_comparisons_peak.csv'\n",
    "summary_df.to_csv(output_file, index=False)\n",
    "print(f\"\\nDetailed comparison data saved to '{output_file}'\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"ANALYSIS COMPLETE\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# ============================================================================\n",
    "# KEY INSIGHTS SUMMARY\n",
    "# ============================================================================\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"KEY INSIGHTS SUMMARY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(f\"\\n1. MOST SIMILAR SHOWS:\")\n",
    "print(f\"   - By pattern (cosine): {squid_game_similar_cosine.index[0]}\")\n",
    "print(f\"   - By magnitude (Euclidean): {squid_game_closest_euclidean.index[0]}\")\n",
    "\n",
    "print(f\"\\n2. MOST DISTINCTIVE EMOTIONS:\")\n",
    "most_distinctive_emotion = sg_distinctiveness.index[0]\n",
    "print(f\"   - Primary: {most_distinctive_emotion} (contributes {sg_distinctiveness.iloc[0]/total_distinctiveness*100:.1f}% to distinctiveness)\")\n",
    "\n",
    "print(f\"\\n3. EXTREME PERCENTILES:\")\n",
    "highest_percentile = percentile_ranks_sorted.index[0]\n",
    "lowest_percentile = percentile_ranks_sorted.index[-1]\n",
    "print(f\"   - Highest: {highest_percentile} ({percentile_ranks_sorted.iloc[0]:.1f}th percentile)\")\n",
    "print(f\"   - Lowest: {lowest_percentile} ({percentile_ranks_sorted.iloc[-1]:.1f}th percentile)\")\n",
    "\n",
    "print(f\"\\n4. POSITION IN EMOTION SPACE:\")\n",
    "print(f\"   - PC1: {sg_pc1:.3f} (explains {pca.explained_variance_ratio_[0]*100:.1f}% of variance)\")\n",
    "print(f\"   - PC2: {sg_pc2:.3f} (explains {pca.explained_variance_ratio_[1]*100:.1f}% of variance)\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46376618-71eb-4357-a66f-b66e5641896e",
   "metadata": {},
   "source": [
    "### Visualizing Emotions over Time during Peak Periods (this code used to produce Figures 18 and 19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3887999-8da0-4303-98b8-e3bfbeb4b6cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Daily Emotion Time Series Visualization\n",
      "================================================================================\n",
      "\n",
      "1. Loading data from Sentiment_Data/Final_InSentiment_Peaks.csv...\n",
      "   Loaded 601,261 rows\n",
      "   Available shows: 39\n",
      "\n",
      "2. Converting timestamps...\n",
      "\n",
      "3. Filtering to show: The Umbrella Academy\n",
      "   Found 17,001 comments for The Umbrella Academy\n",
      "   Date range: 2022-06-23 00:00:00 to 2022-07-19 00:00:00\n",
      "\n",
      "4. Extracting emotion labels from PREDICTION_2 column...\n",
      "   Found 26 unique emotions in the data\n",
      "\n",
      "5. Calculating daily counts for 3 emotions...\n",
      "   Approval: 771 total comments\n",
      "   Sadness: 559 total comments\n",
      "   Realization: 252 total comments\n",
      "   Processed 81 date-emotion combinations\n",
      "\n",
      "6. Creating time series plot...\n",
      "   Saved plot to Sentiment_Data/Temporal_Analysis/The_Umbrella_Academy_emotion_timeseries.png\n",
      "\n",
      "7. Creating stacked area plot...\n",
      "   Saved stacked plot to Sentiment_Data/Temporal_Analysis/The_Umbrella_Academy_emotion_stacked.png\n",
      "\n",
      "8. Summary statistics:\n",
      "             Total  Daily Avg  Max Daily\n",
      "emotion                                 \n",
      "Approval       771      28.56         60\n",
      "Realization    252       9.33         25\n",
      "Sadness        559      20.70         51\n",
      "\n",
      "================================================================================\n",
      "Visualization complete!\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import timedelta\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Configuration\n",
    "INPUT_FILE = \"Data/Final_InSentiment_Peaks.csv\"\n",
    "OUTPUT_DIR = \"Data/Temporal_Analysis\"\n",
    "\n",
    "# Customize these for your analysis\n",
    "SHOW_TO_PLOT = \"Squid Game\"  # Change to any show name\n",
    "EMOTIONS_TO_PLOT = ['Approval', 'Sadness', 'Realization']  # Change as needed\n",
    "\n",
    "print(\"=\" * 80)\n",
    "print(\"Daily Emotion Time Series Visualization\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# Create output directory\n",
    "import os\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "\n",
    "# Load data\n",
    "print(f\"\\n1. Loading data from {INPUT_FILE}...\")\n",
    "df = pd.read_csv(INPUT_FILE)\n",
    "print(f\"   Loaded {len(df):,} rows\")\n",
    "print(f\"   Available shows: {df['SHOW'].nunique()}\")\n",
    "\n",
    "# Convert timestamps\n",
    "print(f\"\\n2. Converting timestamps...\")\n",
    "# Try parsing as formatted string first (e.g., \"4/29/2019 0:00\")\n",
    "df['DATETIME'] = pd.to_datetime(df['TIME_DATETIME'], errors='coerce')\n",
    "\n",
    "# If that didn't work, try Unix timestamp\n",
    "if df['DATETIME'].isna().all():\n",
    "    df['DATETIME'] = pd.to_datetime(df['TIME_DATETIME'], unit='s', errors='coerce')\n",
    "\n",
    "df['DATE'] = df['DATETIME'].dt.date\n",
    "df['DATE'] = pd.to_datetime(df['DATE'])\n",
    "\n",
    "# Filter to specific show\n",
    "print(f\"\\n3. Filtering to show: {SHOW_TO_PLOT}\")\n",
    "show_df = df[df['SHOW'] == SHOW_TO_PLOT].copy()\n",
    "\n",
    "if len(show_df) == 0:\n",
    "    print(f\"\\n   ERROR: Show '{SHOW_TO_PLOT}' not found in dataset!\")\n",
    "    print(f\"\\n   Available shows:\")\n",
    "    for show in sorted(df['SHOW'].unique()):\n",
    "        print(f\"     - {show}\")\n",
    "    exit(1)\n",
    "\n",
    "print(f\"   Found {len(show_df):,} comments for {SHOW_TO_PLOT}\")\n",
    "print(f\"   Date range: {show_df['DATE'].min()} to {show_df['DATE'].max()}\")\n",
    "\n",
    "# Extract emotion labels from PREDICTION_2 column\n",
    "print(f\"\\n4. Extracting emotion labels from PREDICTION_2 column...\")\n",
    "\n",
    "# Parse the PREDICTION_2 column to extract emotion names\n",
    "# Format is like: [27, 'Neutral'] or [3, 'Annoyance']\n",
    "import ast\n",
    "\n",
    "def extract_emotion(pred_str):\n",
    "    \"\"\"Extract emotion name from PREDICTION_2 format\"\"\"\n",
    "    try:\n",
    "        if pd.isna(pred_str):\n",
    "            return None\n",
    "        # Parse the string representation of the list\n",
    "        pred_list = ast.literal_eval(pred_str)\n",
    "        # Return the emotion name (second element)\n",
    "        return pred_list[1] if len(pred_list) > 1 else None\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "show_df['EMOTION'] = show_df['PREDICTION_2'].apply(extract_emotion)\n",
    "\n",
    "# Check available emotions\n",
    "available_emotions = show_df['EMOTION'].unique()\n",
    "available_emotions = [e for e in available_emotions if e is not None]\n",
    "print(f\"   Found {len(available_emotions)} unique emotions in the data\")\n",
    "\n",
    "# Verify requested emotions exist\n",
    "missing_emotions = [e for e in EMOTIONS_TO_PLOT if e not in available_emotions]\n",
    "if missing_emotions:\n",
    "    print(f\"\\n   WARNING: The following emotions are not in the dataset:\")\n",
    "    for e in missing_emotions:\n",
    "        print(f\"     - {e}\")\n",
    "    EMOTIONS_TO_PLOT = [e for e in EMOTIONS_TO_PLOT if e in available_emotions]\n",
    "    print(f\"\\n   Proceeding with: {EMOTIONS_TO_PLOT}\")\n",
    "\n",
    "if len(EMOTIONS_TO_PLOT) == 0:\n",
    "    print(\"\\n   ERROR: No valid emotions to plot!\")\n",
    "    print(f\"\\n   Available emotions in {SHOW_TO_PLOT}:\")\n",
    "    emotion_counts = show_df['EMOTION'].value_counts()\n",
    "    for emotion, count in emotion_counts.head(10).items():\n",
    "        print(f\"     - {emotion}: {count} comments\")\n",
    "    exit(1)\n",
    "\n",
    "# Calculate daily counts for each emotion\n",
    "print(f\"\\n5. Calculating daily counts for {len(EMOTIONS_TO_PLOT)} emotions...\")\n",
    "\n",
    "# For each emotion, count how many comments per day have that emotion\n",
    "daily_emotion_counts = []\n",
    "\n",
    "for emotion in EMOTIONS_TO_PLOT:\n",
    "    emotion_comments = show_df[show_df['EMOTION'] == emotion].copy()\n",
    "    \n",
    "    if len(emotion_comments) == 0:\n",
    "        print(f\"   Warning: No comments found with emotion '{emotion}'\")\n",
    "        continue\n",
    "    \n",
    "    daily_counts = emotion_comments.groupby('DATE').size().reset_index(name='count')\n",
    "    daily_counts['emotion'] = emotion\n",
    "    daily_emotion_counts.append(daily_counts)\n",
    "    print(f\"   {emotion}: {len(emotion_comments)} total comments\")\n",
    "\n",
    "if len(daily_emotion_counts) == 0:\n",
    "    print(\"\\n   ERROR: No emotion data found!\")\n",
    "    exit(1)\n",
    "\n",
    "emotion_timeseries = pd.concat(daily_emotion_counts, ignore_index=True)\n",
    "\n",
    "# Create complete date range for all emotions\n",
    "date_range = pd.date_range(\n",
    "    start=show_df['DATE'].min(),\n",
    "    end=show_df['DATE'].max(),\n",
    "    freq='D'\n",
    ")\n",
    "\n",
    "# Fill in missing dates with 0 counts\n",
    "complete_timeseries = []\n",
    "for emotion in EMOTIONS_TO_PLOT:\n",
    "    emotion_data = emotion_timeseries[emotion_timeseries['emotion'] == emotion].copy()\n",
    "    emotion_data = emotion_data.set_index('DATE')['count'].reindex(date_range, fill_value=0).reset_index()\n",
    "    emotion_data.columns = ['DATE', 'count']\n",
    "    emotion_data['emotion'] = emotion\n",
    "    complete_timeseries.append(emotion_data)\n",
    "\n",
    "emotion_timeseries = pd.concat(complete_timeseries, ignore_index=True)\n",
    "\n",
    "print(f\"   Processed {len(emotion_timeseries)} date-emotion combinations\")\n",
    "\n",
    "# Create visualization\n",
    "print(f\"\\n6. Creating time series plot...\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# Plot each emotion\n",
    "colors = sns.color_palette(\"husl\", len(EMOTIONS_TO_PLOT))\n",
    "\n",
    "for idx, emotion in enumerate(EMOTIONS_TO_PLOT):\n",
    "    emotion_data = emotion_timeseries[emotion_timeseries['emotion'] == emotion]\n",
    "    ax.plot(emotion_data['DATE'], emotion_data['count'], \n",
    "            label=emotion, linewidth=2, marker='o', markersize=4,\n",
    "            color=colors[idx], alpha=0.8)\n",
    "\n",
    "# Format plot\n",
    "ax.set_xlabel('Date', fontsize=12, fontweight='bold')\n",
    "ax.set_ylabel('Daily Comment Count', fontsize=12, fontweight='bold')\n",
    "ax.set_title(f'Daily Emotion Counts: {SHOW_TO_PLOT}\\n(Peak Period: {show_df[\"DATE\"].min().strftime(\"%Y-%m-%d\")} to {show_df[\"DATE\"].max().strftime(\"%Y-%m-%d\")})',\n",
    "             fontsize=14, fontweight='bold', pad=20)\n",
    "ax.legend(loc='best', fontsize=10, framealpha=0.9)\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "# Rotate x-axis labels\n",
    "plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save plot\n",
    "output_filename = f\"{OUTPUT_DIR}/{SHOW_TO_PLOT.replace(' ', '_')}_emotion_timeseries.png\"\n",
    "plt.savefig(output_filename, dpi=150, bbox_inches='tight')\n",
    "print(f\"   Saved plot to {output_filename}\")\n",
    "plt.close()\n",
    "\n",
    "# Create stacked area plot as alternative visualization\n",
    "print(f\"\\n7. Creating stacked area plot...\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# Pivot data for stacked area\n",
    "pivot_data = emotion_timeseries.pivot(index='DATE', columns='emotion', values='count')\n",
    "pivot_data = pivot_data.fillna(0)\n",
    "\n",
    "# Create stacked area plot\n",
    "ax.stackplot(pivot_data.index, \n",
    "             *[pivot_data[emotion] for emotion in EMOTIONS_TO_PLOT],\n",
    "             labels=EMOTIONS_TO_PLOT,\n",
    "             colors=colors,\n",
    "             alpha=0.7)\n",
    "\n",
    "# Format plot\n",
    "ax.set_xlabel('Date', fontsize=12, fontweight='bold')\n",
    "ax.set_ylabel('Daily Comment Count (Stacked)', fontsize=12, fontweight='bold')\n",
    "ax.set_title(f'Stacked Daily Emotion Counts: {SHOW_TO_PLOT}\\n(Peak Period: {show_df[\"DATE\"].min().strftime(\"%Y-%m-%d\")} to {show_df[\"DATE\"].max().strftime(\"%Y-%m-%d\")})',\n",
    "             fontsize=14, fontweight='bold', pad=20)\n",
    "ax.legend(loc='best', fontsize=10, framealpha=0.9)\n",
    "ax.grid(alpha=0.3, axis='y')\n",
    "\n",
    "# Rotate x-axis labels\n",
    "plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save stacked plot\n",
    "stacked_filename = f\"{OUTPUT_DIR}/{SHOW_TO_PLOT.replace(' ', '_')}_emotion_stacked.png\"\n",
    "plt.savefig(stacked_filename, dpi=150, bbox_inches='tight')\n",
    "print(f\"   Saved stacked plot to {stacked_filename}\")\n",
    "plt.close()\n",
    "\n",
    "# Print summary statistics\n",
    "print(f\"\\n8. Summary statistics:\")\n",
    "summary = emotion_timeseries.groupby('emotion')['count'].agg(['sum', 'mean', 'max']).round(2)\n",
    "summary.columns = ['Total', 'Daily Avg', 'Max Daily']\n",
    "print(summary)\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"Visualization complete!\")\n",
    "print(\"=\" * 80)"
   ]
  }
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